Remove 3.9 from CI

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -1,27 +0,0 @@
-As of the time of writing this you need this preview driver for best results:
-https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-PREVIEW.html
-
-HOW TO RUN:
-
-If you have a AMD gpu:
-
-run_amd_gpu.bat
-
-If you have memory issues you can try disabling the smart memory management by running comfyui with:
-
-run_amd_gpu_disable_smart_memory.bat
-
-IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
-
-You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
-
-
-RECOMMENDED WAY TO UPDATE:
-To update the ComfyUI code: update\update_comfyui.bat
-
-
-TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
-In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
-Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
-
-

.ci/windows_amd_base_files/run_amd_gpu.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
-pause

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
-pause

.ci/windows_base_files/README_VERY_IMPORTANT.txt

@@ -4,9 +4,6 @@ if you have a NVIDIA gpu:
 
 run_nvidia_gpu.bat
 
-if you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality):
-
-run_nvidia_gpu_fast_fp16_accumulation.bat
 
 
 To run it in slow CPU mode:

.gitattributes

@@ -1,3 +1,2 @@
 /web/assets/** linguist-generated
 /web/** linguist-vendored
-comfy_api_nodes/apis/__init__.py linguist-generated

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -15,14 +15,6 @@ body:
         steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
 
         If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
-  - type: checkboxes
-    id: custom-nodes-test
-    attributes:
-      label: Custom Node Testing
-      description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
-      options:
-        - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
-          required: false
   - type: textarea
     attributes:
       label: Expected Behavior

.github/ISSUE_TEMPLATE/user-support.yml

@@ -11,14 +11,6 @@ body:
             **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
 
                 If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
-    - type: checkboxes
-      id: custom-nodes-test
-      attributes:
-        label: Custom Node Testing
-        description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
-        options:
-          - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
-            required: false
     - type: textarea
       attributes:
             label: Your question

.github/workflows/check-line-endings.yml

@@ -1,40 +0,0 @@
-name: Check for Windows Line Endings
-
-on:
-  pull_request:
-    branches: ['*'] # Trigger on all pull requests to any branch
-
-jobs:
-  check-line-endings:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0 # Fetch all history to compare changes
-
-      - name: Check for Windows line endings (CRLF)
-        run: |
-          # Get the list of changed files in the PR
-          CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }})
-
-          # Flag to track if CRLF is found
-          CRLF_FOUND=false
-
-          # Loop through each changed file
-          for FILE in $CHANGED_FILES; do
-            # Check if the file exists and is a text file
-            if [ -f "$FILE" ] && file "$FILE" | grep -q "text"; then
-              # Check for CRLF line endings
-              if grep -UP '\r$' "$FILE"; then
-                echo "Error: Windows line endings (CRLF) detected in $FILE"
-                CRLF_FOUND=true
-              fi
-            fi
-          done
-
-          # Exit with error if CRLF was found
-          if [ "$CRLF_FOUND" = true ]; then
-            exit 1
-          fi

.github/workflows/pullrequest-ci-run.yml

@@ -12,7 +12,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [macos, linux, windows]
-        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        python_version: ["3.10", "3.11", "3.12"]
         cuda_version: ["12.1"]
         torch_version: ["stable"]
         include:

.github/workflows/release-stable-all.yml

@@ -1,61 +0,0 @@
-name: "Release Stable All Portable Versions"
-
-on:
-  workflow_dispatch:
-    inputs:
-      git_tag:
-        description: 'Git tag'
-        required: true
-        type: string
-
-jobs:
-  release_nvidia_default:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release NVIDIA Default (cu129)"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu129"
-      python_minor: "13"
-      python_patch: "6"
-      rel_name: "nvidia"
-      rel_extra_name: ""
-      test_release: true
-    secrets: inherit
-
-  release_nvidia_cu128:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release NVIDIA cu128"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "cu128"
-      python_minor: "12"
-      python_patch: "10"
-      rel_name: "nvidia"
-      rel_extra_name: "_cu128"
-      test_release: true
-    secrets: inherit
-
-  release_amd_rocm:
-    permissions:
-      contents: "write"
-      packages: "write"
-      pull-requests: "read"
-    name: "Release AMD ROCm 6.4.4"
-    uses: ./.github/workflows/stable-release.yml
-    with:
-      git_tag: ${{ inputs.git_tag }}
-      cache_tag: "rocm644"
-      python_minor: "12"
-      python_patch: "10"
-      rel_name: "amd"
-      rel_extra_name: ""
-      test_release: false
-    secrets: inherit

.github/workflows/release-webhook.yml

@@ -1,108 +0,0 @@
-name: Release Webhook
-
-on:
-  release:
-    types: [published]
-
-jobs:
-  send-webhook:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Send release webhook
-        env:
-          WEBHOOK_URL: ${{ secrets.RELEASE_GITHUB_WEBHOOK_URL }}
-          WEBHOOK_SECRET: ${{ secrets.RELEASE_GITHUB_WEBHOOK_SECRET }}
-        run: |
-          # Generate UUID for delivery ID
-          DELIVERY_ID=$(uuidgen)
-          HOOK_ID="release-webhook-$(date +%s)"
-          
-          # Create webhook payload matching GitHub release webhook format
-          PAYLOAD=$(cat <<EOF
-          {
-            "action": "published",
-            "release": {
-              "id": ${{ github.event.release.id }},
-              "node_id": "${{ github.event.release.node_id }}",
-              "url": "${{ github.event.release.url }}",
-              "html_url": "${{ github.event.release.html_url }}",
-              "assets_url": "${{ github.event.release.assets_url }}",
-              "upload_url": "${{ github.event.release.upload_url }}",
-              "tag_name": "${{ github.event.release.tag_name }}",
-              "target_commitish": "${{ github.event.release.target_commitish }}",
-              "name": ${{ toJSON(github.event.release.name) }},
-              "body": ${{ toJSON(github.event.release.body) }},
-              "draft": ${{ github.event.release.draft }},
-              "prerelease": ${{ github.event.release.prerelease }},
-              "created_at": "${{ github.event.release.created_at }}",
-              "published_at": "${{ github.event.release.published_at }}",
-              "author": {
-                "login": "${{ github.event.release.author.login }}",
-                "id": ${{ github.event.release.author.id }},
-                "node_id": "${{ github.event.release.author.node_id }}",
-                "avatar_url": "${{ github.event.release.author.avatar_url }}",
-                "url": "${{ github.event.release.author.url }}",
-                "html_url": "${{ github.event.release.author.html_url }}",
-                "type": "${{ github.event.release.author.type }}",
-                "site_admin": ${{ github.event.release.author.site_admin }}
-              },
-              "tarball_url": "${{ github.event.release.tarball_url }}",
-              "zipball_url": "${{ github.event.release.zipball_url }}",
-              "assets": ${{ toJSON(github.event.release.assets) }}
-            },
-            "repository": {
-              "id": ${{ github.event.repository.id }},
-              "node_id": "${{ github.event.repository.node_id }}",
-              "name": "${{ github.event.repository.name }}",
-              "full_name": "${{ github.event.repository.full_name }}",
-              "private": ${{ github.event.repository.private }},
-              "owner": {
-                "login": "${{ github.event.repository.owner.login }}",
-                "id": ${{ github.event.repository.owner.id }},
-                "node_id": "${{ github.event.repository.owner.node_id }}",
-                "avatar_url": "${{ github.event.repository.owner.avatar_url }}",
-                "url": "${{ github.event.repository.owner.url }}",
-                "html_url": "${{ github.event.repository.owner.html_url }}",
-                "type": "${{ github.event.repository.owner.type }}",
-                "site_admin": ${{ github.event.repository.owner.site_admin }}
-              },
-              "html_url": "${{ github.event.repository.html_url }}",
-              "clone_url": "${{ github.event.repository.clone_url }}",
-              "git_url": "${{ github.event.repository.git_url }}",
-              "ssh_url": "${{ github.event.repository.ssh_url }}",
-              "url": "${{ github.event.repository.url }}",
-              "created_at": "${{ github.event.repository.created_at }}",
-              "updated_at": "${{ github.event.repository.updated_at }}",
-              "pushed_at": "${{ github.event.repository.pushed_at }}",
-              "default_branch": "${{ github.event.repository.default_branch }}",
-              "fork": ${{ github.event.repository.fork }}
-            },
-            "sender": {
-              "login": "${{ github.event.sender.login }}",
-              "id": ${{ github.event.sender.id }},
-              "node_id": "${{ github.event.sender.node_id }}",
-              "avatar_url": "${{ github.event.sender.avatar_url }}",
-              "url": "${{ github.event.sender.url }}",
-              "html_url": "${{ github.event.sender.html_url }}",
-              "type": "${{ github.event.sender.type }}",
-              "site_admin": ${{ github.event.sender.site_admin }}
-            }
-          }
-          EOF
-          )
-          
-          # Generate HMAC-SHA256 signature
-          SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$WEBHOOK_SECRET" -hex | cut -d' ' -f2)
-          
-          # Send webhook with required headers
-          curl -X POST "$WEBHOOK_URL" \
-            -H "Content-Type: application/json" \
-            -H "X-GitHub-Event: release" \
-            -H "X-GitHub-Delivery: $DELIVERY_ID" \
-            -H "X-GitHub-Hook-ID: $HOOK_ID" \
-            -H "X-Hub-Signature-256: sha256=$SIGNATURE" \
-            -H "User-Agent: GitHub-Actions-Webhook/1.0" \
-            -d "$PAYLOAD" \
-            --fail --silent --show-error
-          
-          echo "✅ Release webhook sent successfully"

.github/workflows/ruff.yml

@@ -21,28 +21,3 @@ jobs:
 
     - name: Run Ruff
       run: ruff check .
-
-  pylint:
-    name: Run Pylint
-    runs-on: ubuntu-latest
-
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
-
-    - name: Set up Python
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
-
-    - name: Install requirements
-      run: |
-        python -m pip install --upgrade pip
-        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install -r requirements.txt
-
-    - name: Install Pylint
-      run: pip install pylint
-
-    - name: Run Pylint
-      run: pylint comfy_api_nodes

.github/workflows/stable-release.yml

@@ -2,78 +2,28 @@
 name: "Release Stable Version"
 
 on:
-  workflow_call:
-    inputs:
-      git_tag:
-        description: 'Git tag'
-        required: true
-        type: string
-      cache_tag:
-        description: 'Cached dependencies tag'
-        required: true
-        type: string
-        default: "cu129"
-      python_minor:
-        description: 'Python minor version'
-        required: true
-        type: string
-        default: "13"
-      python_patch:
-        description: 'Python patch version'
-        required: true
-        type: string
-        default: "6"
-      rel_name:
-        description: 'Release name'
-        required: true
-        type: string
-        default: "nvidia"
-      rel_extra_name:
-        description: 'Release extra name'
-        required: false
-        type: string
-        default: ""
-      test_release:
-        description: 'Test Release'
-        required: true
-        type: boolean
-        default: true
   workflow_dispatch:
     inputs:
       git_tag:
         description: 'Git tag'
         required: true
         type: string
-      cache_tag:
-        description: 'Cached dependencies tag'
+      cu:
+        description: 'CUDA version'
         required: true
         type: string
-        default: "cu129"
+        default: "128"
       python_minor:
         description: 'Python minor version'
         required: true
         type: string
-        default: "13"
+        default: "12"
       python_patch:
         description: 'Python patch version'
         required: true
         type: string
-        default: "6"
-      rel_name:
-        description: 'Release name'
-        required: true
-        type: string
-        default: "nvidia"
-      rel_extra_name:
-        description: 'Release extra name'
-        required: false
-        type: string
-        default: ""
-      test_release:
-        description: 'Test Release'
-        required: true
-        type: boolean
-        default: true
+        default: "10"
+
 
 jobs:
   package_comfy_windows:
@@ -92,15 +42,15 @@ jobs:
         id: cache
         with:
           path: |
-            ${{ inputs.cache_tag }}_python_deps.tar
+            cu${{ inputs.cu }}_python_deps.tar
             update_comfyui_and_python_dependencies.bat
-          key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
+          key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
       - shell: bash
         run: |
-          mv ${{ inputs.cache_tag }}_python_deps.tar ../
+          mv cu${{ inputs.cu }}_python_deps.tar ../
           mv update_comfyui_and_python_dependencies.bat ../
           cd ..
-          tar xf ${{ inputs.cache_tag }}_python_deps.tar
+          tar xf cu${{ inputs.cu }}_python_deps.tar
           pwd
           ls
 
@@ -115,21 +65,9 @@ jobs:
           echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
           curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
           ./python.exe get-pip.py
-          ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
-
-          grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
-          ./python.exe -s -m pip install -r requirements_comfyui.txt
-          rm requirements_comfyui.txt
-
-          sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
-
-          if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then
-            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
-            rm ./Lib/site-packages/torch/lib/libprotoc.lib
-            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
-          fi
-
-          cd ..
+          ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
+            sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
+            cd ..
 
           git clone --depth 1 https://github.com/comfyanonymous/taesd
           cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/
@@ -142,18 +80,14 @@ jobs:
 
           mkdir update
           cp -r ComfyUI/.ci/update_windows/* ./update/
-          cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
+          cp -r ComfyUI/.ci/windows_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
           cd ..
 
-          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
-          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
+          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
 
-      - shell: bash
-        if: ${{ inputs.test_release }}
-        run: |
-          cd ..
           cd ComfyUI_windows_portable
           python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
 
@@ -162,9 +96,11 @@ jobs:
           ls
 
       - name: Upload binaries to release
-        uses: softprops/action-gh-release@v2
+        uses: svenstaro/upload-release-action@v2
         with:
-          files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
-          tag_name: ${{ inputs.git_tag }}
-          draft: true
-          overwrite_files: true
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          file: ComfyUI_windows_portable_nvidia.7z
+          tag: ${{ inputs.git_tag }}
+          overwrite: true
+          prerelease: true
+          make_latest: false

.github/workflows/test-execution.yml

@@ -1,30 +0,0 @@
-name: Execution Tests
-
-on:
-  push:
-    branches: [ main, master ]
-  pull_request:
-    branches: [ main, master ]
-
-jobs:
-  test:
-    strategy:
-      matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
-    runs-on: ${{ matrix.os }}
-    continue-on-error: true
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python      
-      uses: actions/setup-python@v4
-      with:
-        python-version: '3.12'
-    - name: Install requirements
-      run: |
-        python -m pip install --upgrade pip
-        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-        pip install -r requirements.txt
-        pip install -r tests-unit/requirements.txt
-    - name: Run Execution Tests
-      run: |
-        python -m pytest tests/execution -v --skip-timing-checks

.github/workflows/test-unit.yml

@@ -10,7 +10,7 @@ jobs:
   test:
     strategy:
       matrix:
-        os: [ubuntu-latest, windows-2022, macos-latest]
+        os: [ubuntu-latest, windows-latest, macos-latest]
     runs-on: ${{ matrix.os }}
     continue-on-error: true
     steps:

.github/workflows/windows_release_dependencies.yml

@@ -17,19 +17,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "129"
+        default: "128"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "13"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "6"
+        default: "10"
 #  push:
 #    branches:
 #      - master
@@ -56,8 +56,7 @@ jobs:
             ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > update_comfyui_and_python_dependencies.bat
 
-            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

.github/workflows/windows_release_dependencies_manual.yml

@@ -1,64 +0,0 @@
-name: "Windows Release dependencies Manual"
-
-on:
-  workflow_dispatch:
-    inputs:
-      torch_dependencies:
-        description: 'torch dependencies'
-        required: false
-        type: string
-        default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128"
-      cache_tag:
-        description: 'Cached dependencies tag'
-        required: true
-        type: string
-        default: "cu128"
-
-      python_minor:
-        description: 'python minor version'
-        required: true
-        type: string
-        default: "12"
-
-      python_patch:
-        description: 'python patch version'
-        required: true
-        type: string
-        default: "10"
-
-jobs:
-  build_dependencies:
-    runs-on: windows-latest
-    steps:
-        - uses: actions/checkout@v4
-        - uses: actions/setup-python@v5
-          with:
-            python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
-
-        - shell: bash
-          run: |
-            echo "@echo off
-            call update_comfyui.bat nopause
-            echo -
-            echo This will try to update pytorch and all python dependencies.
-            echo -
-            echo If you just want to update normally, close this and run update_comfyui.bat instead.
-            echo -
-            pause
-            ..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2
-            pause" > update_comfyui_and_python_dependencies.bat
-
-            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
-            python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
-            python -m pip install --no-cache-dir ./temp_wheel_dir/*
-            echo installed basic
-            ls -lah temp_wheel_dir
-            mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps
-            tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps
-
-        - uses: actions/cache/save@v4
-          with:
-            path: |
-              ${{ inputs.cache_tag }}_python_deps.tar
-              update_comfyui_and_python_dependencies.bat
-            key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}

.github/workflows/windows_release_nightly_pytorch.yml

@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "129"
+        default: "128"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "5"
+        default: "2"
 #  push:
 #    branches:
 #      - master
@@ -53,8 +53,6 @@ jobs:
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
-
-            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
             cd ..
 
             git clone --depth 1 https://github.com/comfyanonymous/taesd
@@ -68,7 +66,7 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
+            cp -r ComfyUI/.ci/windows_base_files/* ./
             cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
 
             echo "call update_comfyui.bat nopause

.github/workflows/windows_release_package.yml

@@ -7,19 +7,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "129"
+        default: "128"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "13"
+        default: "12"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "6"
+        default: "10"
 #  push:
 #    branches:
 #      - master
@@ -64,10 +64,6 @@ jobs:
             ./python.exe get-pip.py
             ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
-
-            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
-            rm ./Lib/site-packages/torch/lib/libprotoc.lib
-            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
             cd ..
 
             git clone --depth 1 https://github.com/comfyanonymous/taesd
@@ -81,12 +77,12 @@ jobs:
 
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
+            cp -r ComfyUI/.ci/windows_base_files/* ./
             cp ../update_comfyui_and_python_dependencies.bat ./update/
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
             mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
 
             cd ComfyUI_windows_portable

CODEOWNERS

@@ -1,3 +1,24 @@
 # Admins
 * @comfyanonymous
-* @kosinkadink
+
+# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
+# Inlined the team members for now.
+
+# Maintainers
+*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/requirements.txt @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+/pyproject.toml @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
+
+# Python web server
+/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @christian-byrne
+
+# Node developers
+/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
+/comfy/comfy_types/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

README.md

@@ -6,7 +6,6 @@
 
 [![Website][website-shield]][website-url]
 [![Dynamic JSON Badge][discord-shield]][discord-url]
-[![Twitter][twitter-shield]][twitter-url]
 [![Matrix][matrix-shield]][matrix-url]
 <br>
 [![][github-release-shield]][github-release-link]
@@ -21,8 +20,6 @@
 <!-- Workaround to display total user from https://github.com/badges/shields/issues/4500#issuecomment-2060079995 -->
 [discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
 [discord-url]: https://www.comfy.org/discord
-[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI
-[twitter-url]: https://x.com/ComfyUI
 
 [github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
 [github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
@@ -39,7 +36,7 @@ ComfyUI lets you design and execute advanced stable diffusion pipelines using a
 ## Get Started
 
 #### [Desktop Application](https://www.comfy.org/download)
-- The easiest way to get started.
+- The easiest way to get started. 
 - Available on Windows & macOS.
 
 #### [Windows Portable Package](#installing)
@@ -55,7 +52,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Image Models
-   - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
+   - SD1.x, SD2.x,
    - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
    - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
    - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
@@ -65,20 +62,13 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
    - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
    - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
-   - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
-   - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
-- Image Editing Models
-   - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
-   - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
-   - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)
-   - [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model)
 - Video Models
    - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
    - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
    - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
+   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
    - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
-   - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
 - Audio Models
    - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
    - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -86,10 +76,9 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
    - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
-- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
+- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
-- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
-- Safe loading of ckpt, pt, pth, etc.. files.
+- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
@@ -100,19 +89,20 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
 - [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
 - [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
+- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
 - [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
 - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
-- Works fully offline: core will never download anything unless you want to.
-- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
+- Starts up very fast.
+- Works fully offline: will never download anything.
 - [Config file](extra_model_paths.yaml.example) to set the search paths for models.
 
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
 
 ## Release Process
 
-ComfyUI follows a weekly release cycle targeting Friday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
+ComfyUI follows a weekly release cycle every Friday, with three interconnected repositories:
 
 1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
    - Releases a new stable version (e.g., v0.7.0)
@@ -176,16 +166,14 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
-#### Alternative Downloads:
-
-[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
-
-[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs).
-
 #### How do I share models between another UI and ComfyUI?
 
 See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
 
+## Jupyter Notebook
+
+To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
+
 
 ## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)
 
@@ -197,9 +185,7 @@ comfy install
 
 ## Manual Install (Windows, Linux)
 
-Python 3.14 will work if you comment out the `kornia` dependency in the requirements.txt file (breaks the canny node) and install pytorch nightly but it is not recommended.
-
-Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
+python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.
 
 Git clone this repo.
 
@@ -208,58 +194,48 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
 Put your VAE in: models/vae
 
 
-### AMD GPUs (Linux)
-
+### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.3```
 
-This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0```
-
-
-### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
-
-These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
-
-RDNA 3 (RX 7000 series):
-
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-dgpu/```
-
-RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
-
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
-
-RDNA 4 (RX 9000 series):
-
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
 
 ### Intel GPUs (Windows and Linux)
 
-(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
-
-1. To install PyTorch xpu, use the following command:
-
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```
-
-This is the command to install the Pytorch xpu nightly which might have some performance improvements:
+(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip (currently available in PyTorch nightly builds). More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
+  
+1. To install PyTorch nightly, use the following command:
 
 ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
 
+2. Launch ComfyUI by running `python main.py`
+
+
 (Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
 
-1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
+1. For Intel® Arc™ A-Series Graphics utilizing IPEX, create a conda environment and use the commands below:
+
+```
+conda install libuv
+pip install torch==2.3.1.post0+cxx11.abi torchvision==0.18.1.post0+cxx11.abi torchaudio==2.3.1.post0+cxx11.abi intel-extension-for-pytorch==2.3.110.post0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
+```
+
+For other supported Intel GPUs with IPEX, visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
+
+Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
 
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128```
 
 This is the command to install pytorch nightly instead which might have performance improvements.
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128```
 
 #### Troubleshooting
 
@@ -290,6 +266,10 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
 
+#### DirectML (AMD Cards on Windows)
+
+```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
+
 #### Ascend NPUs
 
 For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:
@@ -307,13 +287,6 @@ For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a
 2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
 3. Launch ComfyUI by running `python main.py`
 
-#### Iluvatar Corex
-
-For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
-
-1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
-2. Launch ComfyUI by running `python main.py`
-
 # Running
 
 ```python main.py```
@@ -364,7 +337,7 @@ Generate a self-signed certificate (not appropriate for shared/production use) a
 
 Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
 
-> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
+> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. 
 <br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
 
 ## Support and dev channel

alembic.ini

@@ -1,84 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-# Use forward slashes (/) also on windows to provide an os agnostic path
-script_location = alembic_db
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to alembic_db/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-# version_path_separator = newline
-#
-# Use os.pathsep. Default configuration used for new projects.
-version_path_separator = os
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-sqlalchemy.url = sqlite:///user/comfyui.db
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = check --fix REVISION_SCRIPT_FILENAME

alembic_db/README.md

@@ -1,4 +0,0 @@
-## Generate new revision
-
-1. Update models in `/app/database/models.py`
-2. Run `alembic revision --autogenerate -m "{your message}"`

alembic_db/env.py

@@ -1,64 +0,0 @@
-from sqlalchemy import engine_from_config
-from sqlalchemy import pool
-
-from alembic import context
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-
-from app.database.models import Base
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline() -> None:
-    """Run migrations in 'offline' mode.
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-    Calls to context.execute() here emit the given string to the
-    script output.
-    """
-    url = config.get_main_option("sqlalchemy.url")
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def run_migrations_online() -> None:
-    """Run migrations in 'online' mode.
-    In this scenario we need to create an Engine
-    and associate a connection with the context.
-    """
-    connectable = engine_from_config(
-        config.get_section(config.config_ini_section, {}),
-        prefix="sqlalchemy.",
-        poolclass=pool.NullPool,
-    )
-
-    with connectable.connect() as connection:
-        context.configure(
-            connection=connection, target_metadata=target_metadata
-        )
-
-        with context.begin_transaction():
-            context.run_migrations()
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    run_migrations_online()

alembic_db/script.py.mako

@@ -1,28 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision: str = ${repr(up_revision)}
-down_revision: Union[str, None] = ${repr(down_revision)}
-branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
-depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    ${downgrades if downgrades else "pass"}

app/database/db.py

@@ -1,112 +0,0 @@
-import logging
-import os
-import shutil
-from app.logger import log_startup_warning
-from utils.install_util import get_missing_requirements_message
-from comfy.cli_args import args
-
-_DB_AVAILABLE = False
-Session = None
-
-
-try:
-    from alembic import command
-    from alembic.config import Config
-    from alembic.runtime.migration import MigrationContext
-    from alembic.script import ScriptDirectory
-    from sqlalchemy import create_engine
-    from sqlalchemy.orm import sessionmaker
-
-    _DB_AVAILABLE = True
-except ImportError as e:
-    log_startup_warning(
-        f"""
-------------------------------------------------------------------------
-Error importing dependencies: {e}
-{get_missing_requirements_message()}
-This error is happening because ComfyUI now uses a local sqlite database.
-------------------------------------------------------------------------
-""".strip()
-    )
-
-
-def dependencies_available():
-    """
-    Temporary function to check if the dependencies are available
-    """
-    return _DB_AVAILABLE
-
-
-def can_create_session():
-    """
-    Temporary function to check if the database is available to create a session
-    During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created
-    """
-    return dependencies_available() and Session is not None
-
-
-def get_alembic_config():
-    root_path = os.path.join(os.path.dirname(__file__), "../..")
-    config_path = os.path.abspath(os.path.join(root_path, "alembic.ini"))
-    scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db"))
-
-    config = Config(config_path)
-    config.set_main_option("script_location", scripts_path)
-    config.set_main_option("sqlalchemy.url", args.database_url)
-
-    return config
-
-
-def get_db_path():
-    url = args.database_url
-    if url.startswith("sqlite:///"):
-        return url.split("///")[1]
-    else:
-        raise ValueError(f"Unsupported database URL '{url}'.")
-
-
-def init_db():
-    db_url = args.database_url
-    logging.debug(f"Database URL: {db_url}")
-    db_path = get_db_path()
-    db_exists = os.path.exists(db_path)
-
-    config = get_alembic_config()
-
-    # Check if we need to upgrade
-    engine = create_engine(db_url)
-    conn = engine.connect()
-
-    context = MigrationContext.configure(conn)
-    current_rev = context.get_current_revision()
-
-    script = ScriptDirectory.from_config(config)
-    target_rev = script.get_current_head()
-
-    if target_rev is None:
-        logging.warning("No target revision found.")
-    elif current_rev != target_rev:
-        # Backup the database pre upgrade
-        backup_path = db_path + ".bkp"
-        if db_exists:
-            shutil.copy(db_path, backup_path)
-        else:
-            backup_path = None
-
-        try:
-            command.upgrade(config, target_rev)
-            logging.info(f"Database upgraded from {current_rev} to {target_rev}")
-        except Exception as e:
-            if backup_path:
-                # Restore the database from backup if upgrade fails
-                shutil.copy(backup_path, db_path)
-                os.remove(backup_path)
-            logging.exception("Error upgrading database: ")
-            raise e
-
-    global Session
-    Session = sessionmaker(bind=engine)
-
-
-def create_session():
-    return Session()

app/database/models.py

@@ -1,14 +0,0 @@
-from sqlalchemy.orm import declarative_base
-
-Base = declarative_base()
-
-
-def to_dict(obj):
-    fields = obj.__table__.columns.keys()
-    return {
-        field: (val.to_dict() if hasattr(val, "to_dict") else val)
-        for field in fields
-        if (val := getattr(obj, field))
-    }
-
-# TODO: Define models here

app/frontend_management.py

@@ -16,63 +16,40 @@ from importlib.metadata import version
 import requests
 from typing_extensions import NotRequired
 
-from utils.install_util import get_missing_requirements_message, requirements_path
-
 from comfy.cli_args import DEFAULT_VERSION_STRING
 import app.logger
 
+# The path to the requirements.txt file
+req_path = Path(__file__).parents[1] / "requirements.txt"
+
 
 def frontend_install_warning_message():
+    """The warning message to display when the frontend version is not up to date."""
+
+    extra = ""
+    if sys.flags.no_user_site:
+        extra = "-s "
     return f"""
-{get_missing_requirements_message()}
+Please install the updated requirements.txt file by running:
+{sys.executable} {extra}-m pip install -r {req_path}
 
 This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+
+If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem
 """.strip()
 
-def parse_version(version: str) -> tuple[int, int, int]:
-        return tuple(map(int, version.split(".")))
-
-def is_valid_version(version: str) -> bool:
-    """Validate if a string is a valid semantic version (X.Y.Z format)."""
-    pattern = r"^(\d+)\.(\d+)\.(\d+)$"
-    return bool(re.match(pattern, version))
-
-def get_installed_frontend_version():
-    """Get the currently installed frontend package version."""
-    frontend_version_str = version("comfyui-frontend-package")
-    return frontend_version_str
-
-
-def get_required_frontend_version():
-    """Get the required frontend version from requirements.txt."""
-    try:
-        with open(requirements_path, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if line.startswith("comfyui-frontend-package=="):
-                    version_str = line.split("==")[-1]
-                    if not is_valid_version(version_str):
-                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
-                        return None
-                    return version_str
-            logging.error("comfyui-frontend-package not found in requirements.txt")
-            return None
-    except FileNotFoundError:
-        logging.error("requirements.txt not found. Cannot determine required frontend version.")
-        return None
-    except Exception as e:
-        logging.error(f"Error reading requirements.txt: {e}")
-        return None
-
 
 def check_frontend_version():
     """Check if the frontend version is up to date."""
 
+    def parse_version(version: str) -> tuple[int, int, int]:
+        return tuple(map(int, version.split(".")))
+
     try:
-        frontend_version_str = get_installed_frontend_version()
+        frontend_version_str = version("comfyui-frontend-package")
         frontend_version = parse_version(frontend_version_str)
-        required_frontend_str = get_required_frontend_version()
-        required_frontend = parse_version(required_frontend_str)
+        with open(req_path, "r", encoding="utf-8") as f:
+            required_frontend = parse_version(f.readline().split("=")[-1])
         if frontend_version < required_frontend:
             app.logger.log_startup_warning(
                 f"""
@@ -144,22 +121,9 @@ class FrontEndProvider:
         response.raise_for_status()  # Raises an HTTPError if the response was an error
         return response.json()
 
-    @cached_property
-    def latest_prerelease(self) -> Release:
-        """Get the latest pre-release version - even if it's older than the latest release"""
-        release = [release for release in self.all_releases if release["prerelease"]]
-
-        if not release:
-            raise ValueError("No pre-releases found")
-
-        # GitHub returns releases in reverse chronological order, so first is latest
-        return release[0]
-
     def get_release(self, version: str) -> Release:
         if version == "latest":
             return self.latest_release
-        elif version == "prerelease":
-            return self.latest_prerelease
         else:
             for release in self.all_releases:
                 if release["tag_name"] in [version, f"v{version}"]:
@@ -200,42 +164,6 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None:
 class FrontendManager:
     CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
 
-    @classmethod
-    def get_required_frontend_version(cls) -> str:
-        """Get the required frontend package version."""
-        return get_required_frontend_version()
-
-    @classmethod
-    def get_installed_templates_version(cls) -> str:
-        """Get the currently installed workflow templates package version."""
-        try:
-            templates_version_str = version("comfyui-workflow-templates")
-            return templates_version_str
-        except Exception:
-            return None
-
-    @classmethod
-    def get_required_templates_version(cls) -> str:
-        """Get the required workflow templates version from requirements.txt."""
-        try:
-            with open(requirements_path, "r", encoding="utf-8") as f:
-                for line in f:
-                    line = line.strip()
-                    if line.startswith("comfyui-workflow-templates=="):
-                        version_str = line.split("==")[-1]
-                        if not is_valid_version(version_str):
-                            logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
-                            return None
-                        return version_str
-                logging.error("comfyui-workflow-templates not found in requirements.txt")
-                return None
-        except FileNotFoundError:
-            logging.error("requirements.txt not found. Cannot determine required templates version.")
-            return None
-        except Exception as e:
-            logging.error(f"Error reading requirements.txt: {e}")
-            return None
-
     @classmethod
     def default_frontend_path(cls) -> str:
         try:
@@ -277,19 +205,6 @@ comfyui-workflow-templates is not installed.
 """.strip()
             )
 
-    @classmethod
-    def embedded_docs_path(cls) -> str:
-        """Get the path to embedded documentation"""
-        try:
-            import comfyui_embedded_docs
-
-            return str(
-                importlib.resources.files(comfyui_embedded_docs) / "docs"
-            )
-        except ImportError:
-            logging.info("comfyui-embedded-docs package not found")
-            return None
-
     @classmethod
     def parse_version_string(cls, value: str) -> tuple[str, str, str]:
         """
@@ -302,7 +217,7 @@ comfyui-workflow-templates is not installed.
         Raises:
             argparse.ArgumentTypeError: If the version string is invalid.
         """
-        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$"
+        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+|latest)$"
         match_result = re.match(VERSION_PATTERN, value)
         if match_result is None:
             raise argparse.ArgumentTypeError(f"Invalid version string: {value}")

app/model_manager.py

@@ -130,21 +130,10 @@ class ModelFileManager:
 
             for file_name in filenames:
                 try:
-                    full_path = os.path.join(dirpath, file_name)
-                    relative_path = os.path.relpath(full_path, directory)
-
-                    # Get file metadata
-                    file_info = {
-                        "name": relative_path,
-                        "pathIndex": pathIndex,
-                        "modified": os.path.getmtime(full_path),  # Add modification time
-                        "created": os.path.getctime(full_path),   # Add creation time
-                        "size": os.path.getsize(full_path)        # Add file size
-                    }
-                    result.append(file_info)
-
-                except Exception as e:
-                    logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
+                    relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+                    result.append(relative_path)
+                except:
+                    logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.")
                     continue
 
             for d in subdirs:
@@ -155,7 +144,7 @@ class ModelFileManager:
                     logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
                     continue
 
-        return result, dirs, time.perf_counter()
+        return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter()
 
     def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
         dirname = os.path.dirname(filepath)

app/user_manager.py

@@ -20,15 +20,13 @@ class FileInfo(TypedDict):
     path: str
     size: int
     modified: int
-    created: int
 
 
 def get_file_info(path: str, relative_to: str) -> FileInfo:
     return {
         "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
         "size": os.path.getsize(path),
-        "modified": os.path.getmtime(path),
-        "created": os.path.getctime(path)
+        "modified": os.path.getmtime(path)
     }
 
 
@@ -363,17 +361,10 @@ class UserManager():
             if not overwrite and os.path.exists(path):
                 return web.Response(status=409, text="File already exists")
 
-            try:
-                body = await request.read()
+            body = await request.read()
 
-                with open(path, "wb") as f:
-                    f.write(body)
-            except OSError as e:
-                logging.warning(f"Error saving file '{path}': {e}")
-                return web.Response(
-                    status=400,
-                    reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|"
-                )
+            with open(path, "wb") as f:
+                f.write(body)
 
             user_path = self.get_request_user_filepath(request, None)
             if full_info:

comfy/audio_encoders/audio_encoders.py

@@ -1,91 +0,0 @@
-from .wav2vec2 import Wav2Vec2Model
-from .whisper import WhisperLargeV3
-import comfy.model_management
-import comfy.ops
-import comfy.utils
-import logging
-import torchaudio
-
-
-class AudioEncoderModel():
-    def __init__(self, config):
-        self.load_device = comfy.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        model_type = config.pop("model_type")
-        model_config = dict(config)
-        model_config.update({
-            "dtype": self.dtype,
-            "device": offload_device,
-            "operations": comfy.ops.manual_cast
-        })
-
-        if model_type == "wav2vec2":
-            self.model = Wav2Vec2Model(**model_config)
-        elif model_type == "whisper3":
-            self.model = WhisperLargeV3(**model_config)
-        self.model.eval()
-        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-        self.model_sample_rate = 16000
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False)
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def encode_audio(self, audio, sample_rate):
-        comfy.model_management.load_model_gpu(self.patcher)
-        audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate)
-        out, all_layers = self.model(audio.to(self.load_device))
-        outputs = {}
-        outputs["encoded_audio"] = out
-        outputs["encoded_audio_all_layers"] = all_layers
-        outputs["audio_samples"] = audio.shape[2]
-        return outputs
-
-
-def load_audio_encoder_from_sd(sd, prefix=""):
-    sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""})
-    if "encoder.layer_norm.bias" in sd: #wav2vec2
-        embed_dim = sd["encoder.layer_norm.bias"].shape[0]
-        if embed_dim == 1024:# large
-            config = {
-                "model_type": "wav2vec2",
-                "embed_dim": 1024,
-                "num_heads": 16,
-                "num_layers": 24,
-                "conv_norm": True,
-                "conv_bias": True,
-                "do_normalize": True,
-                "do_stable_layer_norm": True
-                }
-        elif embed_dim == 768: # base
-            config = {
-                "model_type": "wav2vec2",
-                "embed_dim": 768,
-                "num_heads": 12,
-                "num_layers": 12,
-                "conv_norm": False,
-                "conv_bias": False,
-                "do_normalize": False, # chinese-wav2vec2-base has this False
-                "do_stable_layer_norm": False
-            }
-        else:
-            raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim))
-    elif "model.encoder.embed_positions.weight" in sd:
-        sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""})
-        config = {
-            "model_type": "whisper3",
-        }
-    else:
-        raise RuntimeError("ERROR: audio encoder not supported.")
-
-    audio_encoder = AudioEncoderModel(config)
-    m, u = audio_encoder.load_sd(sd)
-    if len(m) > 0:
-        logging.warning("missing audio encoder: {}".format(m))
-    if len(u) > 0:
-        logging.warning("unexpected audio encoder: {}".format(u))
-
-    return audio_encoder

comfy/audio_encoders/wav2vec2.py

@@ -1,252 +0,0 @@
-import torch
-import torch.nn as nn
-from comfy.ldm.modules.attention import optimized_attention_masked
-
-
-class LayerNormConv(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
-        self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))
-
-class LayerGroupNormConv(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
-        self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return torch.nn.functional.gelu(self.layer_norm(x))
-
-class ConvNoNorm(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.conv(x)
-        return torch.nn.functional.gelu(x)
-
-
-class ConvFeatureEncoder(nn.Module):
-    def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):
-        super().__init__()
-        if conv_norm:
-            self.conv_layers = nn.ModuleList([
-                LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-            ])
-        else:
-            self.conv_layers = nn.ModuleList([
-                LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
-            ])
-
-    def forward(self, x):
-        x = x.unsqueeze(1)
-
-        for conv in self.conv_layers:
-            x = conv(x)
-
-        return x.transpose(1, 2)
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype)
-        self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.layer_norm(x)
-        x = self.projection(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self, embed_dim=768, kernel_size=128, groups=16):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            embed_dim,
-            embed_dim,
-            kernel_size=kernel_size,
-            padding=kernel_size // 2,
-            groups=groups,
-        )
-        self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2)
-        self.activation = nn.GELU()
-
-    def forward(self, x):
-        x = x.transpose(1, 2)
-        x = self.conv(x)[:, :, :-1]
-        x = self.activation(x)
-        x = x.transpose(1, 2)
-        return x
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self,
-        embed_dim=768,
-        num_heads=12,
-        num_layers=12,
-        mlp_ratio=4.0,
-        do_stable_layer_norm=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-
-        self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim)
-        self.layers = nn.ModuleList([
-            TransformerEncoderLayer(
-                embed_dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                do_stable_layer_norm=do_stable_layer_norm,
-                device=device, dtype=dtype, operations=operations
-            )
-            for _ in range(num_layers)
-        ])
-
-        self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)
-        self.do_stable_layer_norm = do_stable_layer_norm
-
-    def forward(self, x, mask=None):
-        x = x + self.pos_conv_embed(x)
-        all_x = ()
-        if not self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-        for layer in self.layers:
-            all_x += (x,)
-            x = layer(x, mask)
-        if self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-        all_x += (x,)
-        return x, all_x
-
-
-class Attention(nn.Module):
-    def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.head_dim = embed_dim // num_heads
-
-        self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-        self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-        self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-        self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
-
-    def forward(self, x, mask=None):
-        assert (mask is None)  # TODO?
-        q = self.q_proj(x)
-        k = self.k_proj(x)
-        v = self.v_proj(x)
-
-        out = optimized_attention_masked(q, k, v, self.num_heads)
-        return self.out_proj(out)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype)
-        self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype)
-
-    def forward(self, x):
-        x = self.intermediate_dense(x)
-        x = torch.nn.functional.gelu(x)
-        x = self.output_dense(x)
-        return x
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        embed_dim=768,
-        num_heads=12,
-        mlp_ratio=4.0,
-        do_stable_layer_norm=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-
-        self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations)
-
-        self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
-        self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
-        self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
-        self.do_stable_layer_norm = do_stable_layer_norm
-
-    def forward(self, x, mask=None):
-        residual = x
-        if self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-        x = self.attention(x, mask=mask)
-        x = residual + x
-        if not self.do_stable_layer_norm:
-            x = self.layer_norm(x)
-            return self.final_layer_norm(x + self.feed_forward(x))
-        else:
-            return x + self.feed_forward(self.final_layer_norm(x))
-
-
-class Wav2Vec2Model(nn.Module):
-    """Complete Wav2Vec 2.0 model."""
-
-    def __init__(
-        self,
-        embed_dim=1024,
-        final_dim=256,
-        num_heads=16,
-        num_layers=24,
-        conv_norm=True,
-        conv_bias=True,
-        do_normalize=True,
-        do_stable_layer_norm=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-
-        conv_dim = 512
-        self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations)
-        self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)
-
-        self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))
-        self.do_normalize = do_normalize
-
-        self.encoder = TransformerEncoder(
-            embed_dim=embed_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            do_stable_layer_norm=do_stable_layer_norm,
-            device=device, dtype=dtype, operations=operations
-        )
-
-    def forward(self, x, mask_time_indices=None, return_dict=False):
-        x = torch.mean(x, dim=1)
-
-        if self.do_normalize:
-            x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
-
-        features = self.feature_extractor(x)
-        features = self.feature_projection(features)
-        batch_size, seq_len, _ = features.shape
-
-        x, all_x = self.encoder(features)
-        return x, all_x

comfy/audio_encoders/whisper.py

@@ -1,186 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchaudio
-from typing import Optional
-from comfy.ldm.modules.attention import optimized_attention_masked
-import comfy.ops
-
-class WhisperFeatureExtractor(nn.Module):
-    def __init__(self, n_mels=128, device=None):
-        super().__init__()
-        self.sample_rate = 16000
-        self.n_fft = 400
-        self.hop_length = 160
-        self.n_mels = n_mels
-        self.chunk_length = 30
-        self.n_samples = 480000
-
-        self.mel_spectrogram = torchaudio.transforms.MelSpectrogram(
-            sample_rate=self.sample_rate,
-            n_fft=self.n_fft,
-            hop_length=self.hop_length,
-            n_mels=self.n_mels,
-            f_min=0,
-            f_max=8000,
-            norm="slaney",
-            mel_scale="slaney",
-        ).to(device)
-
-    def __call__(self, audio):
-        audio = torch.mean(audio, dim=1)
-        batch_size = audio.shape[0]
-        processed_audio = []
-
-        for i in range(batch_size):
-            aud = audio[i]
-            if aud.shape[0] > self.n_samples:
-                aud = aud[:self.n_samples]
-            elif aud.shape[0] < self.n_samples:
-                aud = F.pad(aud, (0, self.n_samples - aud.shape[0]))
-            processed_audio.append(aud)
-
-        audio = torch.stack(processed_audio)
-
-        mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device)
-
-        log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10()
-        log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0)
-        log_mel_spec = (log_mel_spec + 4.0) / 4.0
-
-        return log_mel_spec
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        assert d_model % n_heads == 0
-
-        self.d_model = d_model
-        self.n_heads = n_heads
-        self.d_k = d_model // n_heads
-
-        self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-        self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device)
-        self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-        self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        batch_size, seq_len, _ = query.shape
-
-        q = self.q_proj(query)
-        k = self.k_proj(key)
-        v = self.v_proj(value)
-
-        attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask)
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output
-
-
-class EncoderLayer(nn.Module):
-    def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None):
-        super().__init__()
-
-        self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations)
-        self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
-
-        self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device)
-        self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device)
-        self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None
-    ) -> torch.Tensor:
-        residual = x
-        x = self.self_attn_layer_norm(x)
-        x = self.self_attn(x, x, x, attention_mask)
-        x = residual + x
-
-        residual = x
-        x = self.final_layer_norm(x)
-        x = self.fc1(x)
-        x = F.gelu(x)
-        x = self.fc2(x)
-        x = residual + x
-
-        return x
-
-
-class AudioEncoder(nn.Module):
-    def __init__(
-        self,
-        n_mels: int = 128,
-        n_ctx: int = 1500,
-        n_state: int = 1280,
-        n_head: int = 20,
-        n_layer: int = 32,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-
-        self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device)
-        self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device)
-
-        self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device)
-
-        self.layers = nn.ModuleList([
-            EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations)
-            for _ in range(n_layer)
-        ])
-
-        self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = F.gelu(self.conv1(x))
-        x = F.gelu(self.conv2(x))
-
-        x = x.transpose(1, 2)
-
-        x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x)
-
-        all_x = ()
-        for layer in self.layers:
-            all_x += (x,)
-            x = layer(x)
-
-        x = self.layer_norm(x)
-        all_x += (x,)
-        return x, all_x
-
-
-class WhisperLargeV3(nn.Module):
-    def __init__(
-        self,
-        n_mels: int = 128,
-        n_audio_ctx: int = 1500,
-        n_audio_state: int = 1280,
-        n_audio_head: int = 20,
-        n_audio_layer: int = 32,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-
-        self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device)
-
-        self.encoder = AudioEncoder(
-            n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer,
-            dtype=dtype, device=device, operations=operations
-        )
-
-    def forward(self, audio):
-        mel = self.feature_extractor(audio)
-        x, all_x = self.encoder(mel)
-        return x, all_x

comfy/cli_args.py

@@ -49,8 +49,7 @@ parser.add_argument("--temp-directory", type=str, default=None, help="Set the Co
 parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
-parser.add_argument("--cuda-device", type=str, default=None, metavar="DEVICE_ID", help="Set the ids of cuda devices this instance will use. All other devices will not be visible.")
-parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
+parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
 cm_group = parser.add_mutually_exclusive_group()
 cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
 cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
@@ -89,7 +88,6 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
-parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -132,8 +130,6 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am
 
 parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.")
 
-parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
-
 parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
 
 parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
@@ -143,12 +139,10 @@ class PerformanceFeature(enum.Enum):
     Fp16Accumulation = "fp16_accumulation"
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
-    AutoTune = "autotune"
 
-parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature))))
+parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
 
 parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
-parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")
 
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
 parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
@@ -156,7 +150,6 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win
 
 parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
 parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
-parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.")
 parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
@@ -209,11 +202,6 @@ parser.add_argument(
     help="Set the base URL for the ComfyUI API.  (default: https://api.comfy.org)",
 )
 
-database_default_path = os.path.abspath(
-    os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
-)
-parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
-
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:

comfy/clip_model.py

@@ -61,12 +61,8 @@ class CLIPEncoder(torch.nn.Module):
     def forward(self, x, mask=None, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
 
-        all_intermediate = None
         if intermediate_output is not None:
-            if intermediate_output == "all":
-                all_intermediate = []
-                intermediate_output = None
-            elif intermediate_output < 0:
+            if intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
         intermediate = None
@@ -74,12 +70,6 @@ class CLIPEncoder(torch.nn.Module):
             x = l(x, mask, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
-            if all_intermediate is not None:
-                all_intermediate.append(x.unsqueeze(1).clone())
-
-        if all_intermediate is not None:
-            intermediate = torch.cat(all_intermediate, dim=1)
-
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):
@@ -107,7 +97,7 @@ class CLIPTextModel_(torch.nn.Module):
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]):
+    def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
         if embeds is not None:
             x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device)
         else:

comfy/clip_vision.py

@@ -50,13 +50,7 @@ class ClipVisionModel():
         self.image_size = config.get("image_size", 224)
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
         self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
-        model_type = config.get("model_type", "clip_vision_model")
-        model_class = IMAGE_ENCODERS.get(model_type)
-        if model_type == "siglip_vision_model":
-            self.return_all_hidden_states = True
-        else:
-            self.return_all_hidden_states = False
-
+        model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model"))
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
@@ -74,18 +68,12 @@ class ClipVisionModel():
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
         pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
-        out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
+        out = self.model(pixel_values=pixel_values, intermediate_output=-2)
 
         outputs = Output()
         outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
         outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
-        if self.return_all_hidden_states:
-            all_hs = out[1].to(comfy.model_management.intermediate_device())
-            outputs["penultimate_hidden_states"] = all_hs[:, -2]
-            outputs["all_hidden_states"] = all_hs
-        else:
-            outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
-
+        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
         outputs["mm_projected"] = out[3]
         return outputs
 
@@ -136,12 +124,8 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
         else:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
-
-    # Dinov2
-    elif 'encoder.layer.39.layer_scale2.lambda1' in sd:
+    elif "embeddings.patch_embeddings.projection.weight" in sd:
         json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
-    elif 'encoder.layer.23.layer_scale2.lambda1' in sd:
-        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json")
     else:
         return None
 

comfy/comfy_types/node_typing.py

@@ -37,8 +37,6 @@ class IO(StrEnum):
     CONTROL_NET = "CONTROL_NET"
     VAE = "VAE"
     MODEL = "MODEL"
-    LORA_MODEL = "LORA_MODEL"
-    LOSS_MAP = "LOSS_MAP"
     CLIP_VISION = "CLIP_VISION"
     CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT"
     STYLE_MODEL = "STYLE_MODEL"

comfy/conds.py

@@ -1,7 +1,6 @@
 import torch
 import math
 import comfy.utils
-import logging
 
 
 class CONDRegular:
@@ -11,15 +10,12 @@ class CONDRegular:
     def _copy_with(self, cond):
         return self.__class__(cond)
 
-    def process_cond(self, batch_size, **kwargs):
-        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size))
+    def process_cond(self, batch_size, device, **kwargs):
+        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
 
     def can_concat(self, other):
         if self.cond.shape != other.cond.shape:
             return False
-        if self.cond.device != other.cond.device:
-            logging.warning("WARNING: conds not on same device, skipping concat.")
-            return False
         return True
 
     def concat(self, others):
@@ -28,19 +24,15 @@ class CONDRegular:
             conds.append(x.cond)
         return torch.cat(conds)
 
-    def size(self):
-        return list(self.cond.size())
-
-
 class CONDNoiseShape(CONDRegular):
-    def process_cond(self, batch_size, area, **kwargs):
+    def process_cond(self, batch_size, device, area, **kwargs):
         data = self.cond
         if area is not None:
             dims = len(area) // 2
             for i in range(dims):
                 data = data.narrow(i + 2, area[i + dims], area[i])
 
-        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size))
+        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
 
 
 class CONDCrossAttn(CONDRegular):
@@ -55,9 +47,6 @@ class CONDCrossAttn(CONDRegular):
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
-        if self.cond.device != other.cond.device:
-            logging.warning("WARNING: conds not on same device: skipping concat.")
-            return False
         return True
 
     def concat(self, others):
@@ -75,12 +64,11 @@ class CONDCrossAttn(CONDRegular):
             out.append(c)
         return torch.cat(out)
 
-
 class CONDConstant(CONDRegular):
     def __init__(self, cond):
         self.cond = cond
 
-    def process_cond(self, batch_size, **kwargs):
+    def process_cond(self, batch_size, device, **kwargs):
         return self._copy_with(self.cond)
 
     def can_concat(self, other):
@@ -90,48 +78,3 @@ class CONDConstant(CONDRegular):
 
     def concat(self, others):
         return self.cond
-
-    def size(self):
-        return [1]
-
-
-class CONDList(CONDRegular):
-    def __init__(self, cond):
-        self.cond = cond
-
-    def process_cond(self, batch_size, **kwargs):
-        out = []
-        for c in self.cond:
-            out.append(comfy.utils.repeat_to_batch_size(c, batch_size))
-
-        return self._copy_with(out)
-
-    def can_concat(self, other):
-        if len(self.cond) != len(other.cond):
-            return False
-        for i in range(len(self.cond)):
-            if self.cond[i].shape != other.cond[i].shape:
-                return False
-
-        return True
-
-    def concat(self, others):
-        out = []
-        for i in range(len(self.cond)):
-            o = [self.cond[i]]
-            for x in others:
-                o.append(x.cond[i])
-            out.append(torch.cat(o))
-
-        return out
-
-    def size(self):  # hackish implementation to make the mem estimation work
-        o = 0
-        c = 1
-        for c in self.cond:
-            size = c.size()
-            o += math.prod(size)
-            if len(size) > 1:
-                c = size[1]
-
-        return [1, c, o // c]

comfy/context_windows.py

@@ -1,540 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, Callable
-import torch
-import numpy as np
-import collections
-from dataclasses import dataclass
-from abc import ABC, abstractmethod
-import logging
-import comfy.model_management
-import comfy.patcher_extension
-if TYPE_CHECKING:
-    from comfy.model_base import BaseModel
-    from comfy.model_patcher import ModelPatcher
-    from comfy.controlnet import ControlBase
-
-
-class ContextWindowABC(ABC):
-    def __init__(self):
-        ...
-
-    @abstractmethod
-    def get_tensor(self, full: torch.Tensor) -> torch.Tensor:
-        """
-        Get torch.Tensor applicable to current window.
-        """
-        raise NotImplementedError("Not implemented.")
-
-    @abstractmethod
-    def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torch.Tensor:
-        """
-        Apply torch.Tensor of window to the full tensor, in place. Returns reference to updated full tensor, not a copy.
-        """
-        raise NotImplementedError("Not implemented.")
-
-class ContextHandlerABC(ABC):
-    def __init__(self):
-        ...
-
-    @abstractmethod
-    def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
-        raise NotImplementedError("Not implemented.")
-
-    @abstractmethod
-    def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: ContextWindowABC, device=None) -> list:
-        raise NotImplementedError("Not implemented.")
-
-    @abstractmethod
-    def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
-        raise NotImplementedError("Not implemented.")
-
-
-
-class IndexListContextWindow(ContextWindowABC):
-    def __init__(self, index_list: list[int], dim: int=0):
-        self.index_list = index_list
-        self.context_length = len(index_list)
-        self.dim = dim
-
-    def get_tensor(self, full: torch.Tensor, device=None, dim=None) -> torch.Tensor:
-        if dim is None:
-            dim = self.dim
-        if dim == 0 and full.shape[dim] == 1:
-            return full
-        idx = [slice(None)] * dim + [self.index_list]
-        return full[idx].to(device)
-
-    def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:
-        if dim is None:
-            dim = self.dim
-        idx = [slice(None)] * dim + [self.index_list]
-        full[idx] += to_add
-        return full
-
-
-class IndexListCallbacks:
-    EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows"
-    COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results"
-    EXECUTE_START = "execute_start"
-    EXECUTE_CLEANUP = "execute_cleanup"
-
-    def init_callbacks(self):
-        return {}
-
-
-@dataclass
-class ContextSchedule:
-    name: str
-    func: Callable
-
-@dataclass
-class ContextFuseMethod:
-    name: str
-    func: Callable
-
-ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
-class IndexListContextHandler(ContextHandlerABC):
-    def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop=False, dim=0):
-        self.context_schedule = context_schedule
-        self.fuse_method = fuse_method
-        self.context_length = context_length
-        self.context_overlap = context_overlap
-        self.context_stride = context_stride
-        self.closed_loop = closed_loop
-        self.dim = dim
-        self._step = 0
-
-        self.callbacks = {}
-
-    def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
-        # for now, assume first dim is batch - should have stored on BaseModel in actual implementation
-        if x_in.size(self.dim) > self.context_length:
-            logging.info(f"Using context windows {self.context_length} for {x_in.size(self.dim)} frames.")
-            return True
-        return False
-
-    def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase:
-        if control.previous_controlnet is not None:
-            self.prepare_control_objects(control.previous_controlnet, device)
-        return control
-
-    def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list:
-        if cond_in is None:
-            return None
-        # reuse or resize cond items to match context requirements
-        resized_cond = []
-        # cond object is a list containing a dict - outer list is irrelevant, so just loop through it
-        for actual_cond in cond_in:
-            resized_actual_cond = actual_cond.copy()
-            # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary
-            for key in actual_cond:
-                try:
-                    cond_item = actual_cond[key]
-                    if isinstance(cond_item, torch.Tensor):
-                        # check that tensor is the expected length - x.size(0)
-                        if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim):
-                            # if so, it's subsetting time - tell controls the expected indeces so they can handle them
-                            actual_cond_item = window.get_tensor(cond_item)
-                            resized_actual_cond[key] = actual_cond_item.to(device)
-                        else:
-                            resized_actual_cond[key] = cond_item.to(device)
-                    # look for control
-                    elif key == "control":
-                        resized_actual_cond[key] = self.prepare_control_objects(cond_item, device)
-                    elif isinstance(cond_item, dict):
-                        new_cond_item = cond_item.copy()
-                        # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
-                        for cond_key, cond_value in new_cond_item.items():
-                            if isinstance(cond_value, torch.Tensor):
-                                if cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim):
-                                    new_cond_item[cond_key] = window.get_tensor(cond_value, device)
-                            # if has cond that is a Tensor, check if needs to be subset
-                            elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
-                                if cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim):
-                                    new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device))
-                            elif cond_key == "num_video_frames": # for SVD
-                                new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
-                                new_cond_item[cond_key].cond = window.context_length
-                        resized_actual_cond[key] = new_cond_item
-                    else:
-                        resized_actual_cond[key] = cond_item
-                finally:
-                    del cond_item  # just in case to prevent VRAM issues
-            resized_cond.append(resized_actual_cond)
-        return resized_cond
-
-    def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
-        mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001)
-        matches = torch.nonzero(mask)
-        if torch.numel(matches) == 0:
-            raise Exception("No sample_sigmas matched current timestep; something went wrong.")
-        self._step = int(matches[0].item())
-
-    def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
-        full_length = x_in.size(self.dim) # TODO: choose dim based on model
-        context_windows = self.context_schedule.func(full_length, self, model_options)
-        context_windows = [IndexListContextWindow(window, dim=self.dim) for window in context_windows]
-        return context_windows
-
-    def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
-        self.set_step(timestep, model_options)
-        context_windows = self.get_context_windows(model, x_in, model_options)
-        enumerated_context_windows = list(enumerate(context_windows))
-
-        conds_final = [torch.zeros_like(x_in) for _ in conds]
-        if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-            counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
-        else:
-            counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds]
-        biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds]
-
-        for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks):
-            callback(self, model, x_in, conds, timestep, model_options)
-
-        for enum_window in enumerated_context_windows:
-            results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options)
-            for result in results:
-                self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep,
-                                            conds_final, counts_final, biases_final)
-        try:
-            # finalize conds
-            if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-                # relative is already normalized, so return as is
-                del counts_final
-                return conds_final
-            else:
-                # normalize conds via division by context usage counts
-                for i in range(len(conds_final)):
-                    conds_final[i] /= counts_final[i]
-                del counts_final
-                return conds_final
-        finally:
-            for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks):
-                callback(self, model, x_in, conds, timestep, model_options)
-
-    def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]],
-                                model_options, device=None, first_device=None):
-        results: list[ContextResults] = []
-        for window_idx, window in enumerated_context_windows:
-            # allow processing to end between context window executions for faster Cancel
-            comfy.model_management.throw_exception_if_processing_interrupted()
-
-            for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks):
-                callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device)
-
-            # update exposed params
-            model_options["transformer_options"]["context_window"] = window
-            # get subsections of x, timestep, conds
-            sub_x = window.get_tensor(x_in, device)
-            sub_timestep = window.get_tensor(timestep, device, dim=0)
-            sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds]
-
-            sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options)
-            if device is not None:
-                for i in range(len(sub_conds_out)):
-                    sub_conds_out[i] = sub_conds_out[i].to(x_in.device)
-            results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window))
-        return results
-
-
-    def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor,
-                                    conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]):
-        if self.fuse_method.name == ContextFuseMethods.RELATIVE:
-            for pos, idx in enumerate(window.index_list):
-                # bias is the influence of a specific index in relation to the whole context window
-                bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2)
-                bias = max(1e-2, bias)
-                # take weighted average relative to total bias of current idx
-                for i in range(len(sub_conds_out)):
-                    bias_total = biases_final[i][idx]
-                    prev_weight = (bias_total / (bias_total + bias))
-                    new_weight = (bias / (bias_total + bias))
-                    # account for dims of tensors
-                    idx_window = [slice(None)] * self.dim + [idx]
-                    pos_window = [slice(None)] * self.dim + [pos]
-                    # apply new values
-                    conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight
-                    biases_final[i][idx] = bias_total + bias
-        else:
-            # add conds and counts based on weights of fuse method
-            weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep)
-            weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device)
-            for i in range(len(sub_conds_out)):
-                window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor)
-                window.add_window(counts_final[i], weights_tensor)
-
-        for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks):
-            callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final)
-
-
-def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs):
-    # limit noise_shape length to context_length for more accurate vram use estimation
-    model_options = kwargs.get("model_options", None)
-    if model_options is None:
-        raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.")
-    handler: IndexListContextHandler = model_options.get("context_handler", None)
-    if handler is not None:
-        noise_shape = list(noise_shape)
-        noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length)
-    return executor(model, noise_shape, *args, **kwargs)
-
-
-def create_prepare_sampling_wrapper(model: ModelPatcher):
-    model.add_wrapper_with_key(
-        comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING,
-        "ContextWindows_prepare_sampling",
-        _prepare_sampling_wrapper
-    )
-
-
-def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:
-    total_dims = len(x_in.shape)
-    weights_tensor = torch.Tensor(weights).to(device=device)
-    for _ in range(dim):
-        weights_tensor = weights_tensor.unsqueeze(0)
-    for _ in range(total_dims - dim - 1):
-        weights_tensor = weights_tensor.unsqueeze(-1)
-    return weights_tensor
-
-def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]:
-    total_dims = len(x_in.shape)
-    shape = []
-    for _ in range(dim):
-        shape.append(1)
-    shape.append(x_in.shape[dim])
-    for _ in range(total_dims - dim - 1):
-        shape.append(1)
-    return shape
-
-class ContextSchedules:
-    UNIFORM_LOOPED = "looped_uniform"
-    UNIFORM_STANDARD = "standard_uniform"
-    STATIC_STANDARD = "standard_static"
-    BATCHED = "batched"
-
-
-# from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py
-def create_windows_uniform_looped(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    windows = []
-    if num_frames < handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-
-    context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1)
-    # obtain uniform windows as normal, looping and all
-    for context_step in 1 << np.arange(context_stride):
-        pad = int(round(num_frames * ordered_halving(handler._step)))
-        for j in range(
-            int(ordered_halving(handler._step) * context_step) + pad,
-            num_frames + pad + (0 if handler.closed_loop else -handler.context_overlap),
-            (handler.context_length * context_step - handler.context_overlap),
-        ):
-            windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)])
-
-    return windows
-
-def create_windows_uniform_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning;
-    # instead, they get shifted to the corresponding end of the frames.
-    # in the case that a window (shifted or not) is identical to the previous one, it gets skipped.
-    windows = []
-    if num_frames <= handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-
-    context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1)
-    # first, obtain uniform windows as normal, looping and all
-    for context_step in 1 << np.arange(context_stride):
-        pad = int(round(num_frames * ordered_halving(handler._step)))
-        for j in range(
-            int(ordered_halving(handler._step) * context_step) + pad,
-            num_frames + pad + (-handler.context_overlap),
-            (handler.context_length * context_step - handler.context_overlap),
-        ):
-            windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)])
-
-    # now that windows are created, shift any windows that loop, and delete duplicate windows
-    delete_idxs = []
-    win_i = 0
-    while win_i < len(windows):
-        # if window is rolls over itself, need to shift it
-        is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames)
-        if is_roll:
-            roll_val = windows[win_i][roll_idx]  # roll_val might not be 0 for windows of higher strides
-            shift_window_to_end(windows[win_i], num_frames=num_frames)
-            # check if next window (cyclical) is missing roll_val
-            if roll_val not in windows[(win_i+1) % len(windows)]:
-                # need to insert new window here - just insert window starting at roll_val
-                windows.insert(win_i+1, list(range(roll_val, roll_val + handler.context_length)))
-        # delete window if it's not unique
-        for pre_i in range(0, win_i):
-            if windows[win_i] == windows[pre_i]:
-                delete_idxs.append(win_i)
-                break
-        win_i += 1
-
-    # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation
-    delete_idxs.reverse()
-    for i in delete_idxs:
-        windows.pop(i)
-
-    return windows
-
-
-def create_windows_static_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    windows = []
-    if num_frames <= handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-    # always return the same set of windows
-    delta = handler.context_length - handler.context_overlap
-    for start_idx in range(0, num_frames, delta):
-        # if past the end of frames, move start_idx back to allow same context_length
-        ending = start_idx + handler.context_length
-        if ending >= num_frames:
-            final_delta = ending - num_frames
-            final_start_idx = start_idx - final_delta
-            windows.append(list(range(final_start_idx, final_start_idx + handler.context_length)))
-            break
-        windows.append(list(range(start_idx, start_idx + handler.context_length)))
-    return windows
-
-
-def create_windows_batched(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]):
-    windows = []
-    if num_frames <= handler.context_length:
-        windows.append(list(range(num_frames)))
-        return windows
-    # always return the same set of windows;
-    # no overlap, just cut up based on context_length;
-    # last window size will be different if num_frames % opts.context_length != 0
-    for start_idx in range(0, num_frames, handler.context_length):
-        windows.append(list(range(start_idx, min(start_idx + handler.context_length, num_frames))))
-    return windows
-
-
-def create_windows_default(num_frames: int, handler: IndexListContextHandler):
-    return [list(range(num_frames))]
-
-
-CONTEXT_MAPPING = {
-    ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped,
-    ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard,
-    ContextSchedules.STATIC_STANDARD: create_windows_static_standard,
-    ContextSchedules.BATCHED: create_windows_batched,
-}
-
-
-def get_matching_context_schedule(context_schedule: str) -> ContextSchedule:
-    func = CONTEXT_MAPPING.get(context_schedule, None)
-    if func is None:
-        raise ValueError(f"Unknown context_schedule '{context_schedule}'.")
-    return ContextSchedule(context_schedule, func)
-
-
-def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None):
-    return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs)
-
-
-def create_weights_flat(length: int, **kwargs) -> list[float]:
-    # weight is the same for all
-    return [1.0] * length
-
-def create_weights_pyramid(length: int, **kwargs) -> list[float]:
-    # weight is based on the distance away from the edge of the context window;
-    # based on weighted average concept in FreeNoise paper
-    if length % 2 == 0:
-        max_weight = length // 2
-        weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1))
-    else:
-        max_weight = (length + 1) // 2
-        weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1))
-    return weight_sequence
-
-def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs):
-    # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302
-    # only expected overlap is given different weights
-    weights_torch = torch.ones((length))
-    # blend left-side on all except first window
-    if min(idxs) > 0:
-        ramp_up = torch.linspace(1e-37, 1, handler.context_overlap)
-        weights_torch[:handler.context_overlap] = ramp_up
-    # blend right-side on all except last window
-    if max(idxs) < full_length-1:
-        ramp_down = torch.linspace(1, 1e-37, handler.context_overlap)
-        weights_torch[-handler.context_overlap:] = ramp_down
-    return weights_torch
-
-class ContextFuseMethods:
-    FLAT = "flat"
-    PYRAMID = "pyramid"
-    RELATIVE = "relative"
-    OVERLAP_LINEAR = "overlap-linear"
-
-    LIST = [PYRAMID, FLAT, OVERLAP_LINEAR]
-    LIST_STATIC = [PYRAMID, RELATIVE, FLAT, OVERLAP_LINEAR]
-
-
-FUSE_MAPPING = {
-    ContextFuseMethods.FLAT: create_weights_flat,
-    ContextFuseMethods.PYRAMID: create_weights_pyramid,
-    ContextFuseMethods.RELATIVE: create_weights_pyramid,
-    ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear,
-}
-
-def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod:
-    func = FUSE_MAPPING.get(fuse_method, None)
-    if func is None:
-        raise ValueError(f"Unknown fuse_method '{fuse_method}'.")
-    return ContextFuseMethod(fuse_method, func)
-
-# Returns fraction that has denominator that is a power of 2
-def ordered_halving(val):
-    # get binary value, padded with 0s for 64 bits
-    bin_str = f"{val:064b}"
-    # flip binary value, padding included
-    bin_flip = bin_str[::-1]
-    # convert binary to int
-    as_int = int(bin_flip, 2)
-    # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616,
-    # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's)
-    return as_int / (1 << 64)
-
-
-def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]:
-    all_indexes = list(range(num_frames))
-    for w in windows:
-        for val in w:
-            try:
-                all_indexes.remove(val)
-            except ValueError:
-                pass
-    return all_indexes
-
-
-def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]:
-    prev_val = -1
-    for i, val in enumerate(window):
-        val = val % num_frames
-        if val < prev_val:
-            return True, i
-        prev_val = val
-    return False, -1
-
-
-def shift_window_to_start(window: list[int], num_frames: int):
-    start_val = window[0]
-    for i in range(len(window)):
-        # 1) subtract each element by start_val to move vals relative to the start of all frames
-        # 2) add num_frames and take modulus to get adjusted vals
-        window[i] = ((window[i] - start_val) + num_frames) % num_frames
-
-
-def shift_window_to_end(window: list[int], num_frames: int):
-    # 1) shift window to start
-    shift_window_to_start(window, num_frames)
-    end_val = window[-1]
-    end_delta = num_frames - end_val - 1
-    for i in range(len(window)):
-        # 2) add end_delta to each val to slide windows to end
-        window[i] = window[i] + end_delta

comfy/controlnet.py

@@ -15,21 +15,19 @@
     You should have received a copy of the GNU General Public License
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
-from __future__ import annotations
+
 
 import torch
 from enum import Enum
 import math
 import os
 import logging
-import copy
 import comfy.utils
 import comfy.model_management
 import comfy.model_detection
 import comfy.model_patcher
 import comfy.ops
 import comfy.latent_formats
-import comfy.model_base
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
@@ -37,15 +35,15 @@ import comfy.ldm.cascade.controlnet
 import comfy.cldm.mmdit
 import comfy.ldm.hydit.controlnet
 import comfy.ldm.flux.controlnet
-import comfy.ldm.qwen_image.controlnet
 import comfy.cldm.dit_embedder
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING
 if TYPE_CHECKING:
     from comfy.hooks import HookGroup
 
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
+    #print(current_batch_size, target_batch_size)
     if current_batch_size == 1:
         return tensor
 
@@ -65,18 +63,6 @@ class StrengthType(Enum):
     CONSTANT = 1
     LINEAR_UP = 2
 
-class ControlIsolation:
-    '''Temporarily set a ControlBase object's previous_controlnet to None to prevent cascading calls.'''
-    def __init__(self, control: ControlBase):
-        self.control = control
-        self.orig_previous_controlnet = control.previous_controlnet
-
-    def __enter__(self):
-        self.control.previous_controlnet = None
-
-    def __exit__(self, *args):
-        self.control.previous_controlnet = self.orig_previous_controlnet
-
 class ControlBase:
     def __init__(self):
         self.cond_hint_original = None
@@ -90,7 +76,7 @@ class ControlBase:
         self.compression_ratio = 8
         self.upscale_algorithm = 'nearest-exact'
         self.extra_args = {}
-        self.previous_controlnet: Union[ControlBase, None] = None
+        self.previous_controlnet = None
         self.extra_conds = []
         self.strength_type = StrengthType.CONSTANT
         self.concat_mask = False
@@ -98,7 +84,6 @@ class ControlBase:
         self.extra_concat = None
         self.extra_hooks: HookGroup = None
         self.preprocess_image = lambda a: a
-        self.multigpu_clones: dict[torch.device, ControlBase] = {}
 
     def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
@@ -125,38 +110,17 @@ class ControlBase:
     def cleanup(self):
         if self.previous_controlnet is not None:
             self.previous_controlnet.cleanup()
-        for device_cnet in self.multigpu_clones.values():
-            with ControlIsolation(device_cnet):
-                device_cnet.cleanup()
+
         self.cond_hint = None
         self.extra_concat = None
         self.timestep_range = None
 
     def get_models(self):
         out = []
-        for device_cnet in self.multigpu_clones.values():
-            out += device_cnet.get_models_only_self()
         if self.previous_controlnet is not None:
             out += self.previous_controlnet.get_models()
         return out
 
-    def get_models_only_self(self):
-        'Calls get_models, but temporarily sets previous_controlnet to None.'
-        with ControlIsolation(self):
-            return self.get_models()
-
-    def get_instance_for_device(self, device):
-        'Returns instance of this Control object intended for selected device.'
-        return self.multigpu_clones.get(device, self)
-
-    def deepclone_multigpu(self, load_device, autoregister=False):
-        '''
-        Create deep clone of Control object where model(s) is set to other devices.
-
-        When autoregister is set to True, the deep clone is also added to multigpu_clones dict.
-        '''
-        raise NotImplementedError("Classes inheriting from ControlBase should define their own deepclone_multigpu funtion.")
-
     def get_extra_hooks(self):
         out = []
         if self.extra_hooks is not None:
@@ -165,7 +129,7 @@ class ControlBase:
             out += self.previous_controlnet.get_extra_hooks()
         return out
 
-    def copy_to(self, c: ControlBase):
+    def copy_to(self, c):
         c.cond_hint_original = self.cond_hint_original
         c.strength = self.strength
         c.timestep_percent_range = self.timestep_percent_range
@@ -272,11 +236,11 @@ class ControlNet(ControlBase):
             self.cond_hint = None
             compression_ratio = self.compression_ratio
             if self.vae is not None:
-                compression_ratio *= self.vae.spacial_compression_encode()
+                compression_ratio *= self.vae.downscale_ratio
             else:
                 if self.latent_format is not None:
                     raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center")
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
             self.cond_hint = self.preprocess_image(self.cond_hint)
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
@@ -288,10 +252,7 @@ class ControlNet(ControlBase):
                 to_concat = []
                 for c in self.extra_concat_orig:
                     c = c.to(self.cond_hint.device)
-                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[-1], self.cond_hint.shape[-2], self.upscale_algorithm, "center")
-                    if c.ndim < self.cond_hint.ndim:
-                        c = c.unsqueeze(2)
-                        c = comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[2], dim=2)
+                    c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
                     to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
                 self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
 
@@ -304,12 +265,12 @@ class ControlNet(ControlBase):
         for c in self.extra_conds:
             temp = cond.get(c, None)
             if temp is not None:
-                extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device)
+                extra[c] = temp.to(dtype)
 
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
-        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra)
+        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
         return self.control_merge(control, control_prev, output_dtype=None)
 
     def copy(self):
@@ -319,14 +280,6 @@ class ControlNet(ControlBase):
         self.copy_to(c)
         return c
 
-    def deepclone_multigpu(self, load_device, autoregister=False):
-        c = self.copy()
-        c.control_model = copy.deepcopy(c.control_model)
-        c.control_model_wrapped = comfy.model_patcher.ModelPatcher(c.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
-        if autoregister:
-            self.multigpu_clones[load_device] = c
-        return c
-
     def get_models(self):
         out = super().get_models()
         out.append(self.control_model_wrapped)
@@ -437,9 +390,8 @@ class ControlLora(ControlNet):
                 pass
 
         for k in self.control_weights:
-            if (k not in {"lora_controlnet"}):
-                if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k):
-                    comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
+            if k not in {"lora_controlnet"}:
+                comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
 
     def copy(self):
         c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
@@ -629,22 +581,6 @@ def load_controlnet_flux_instantx(sd, model_options={}):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
-def load_controlnet_qwen_instantx(sd, model_options={}):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
-    control_latent_channels = sd.get("controlnet_x_embedder.weight").shape[1]
-
-    extra_condition_channels = 0
-    concat_mask = False
-    if control_latent_channels == 68: #inpaint controlnet
-        extra_condition_channels = control_latent_channels - 64
-        concat_mask = True
-    control_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(extra_condition_channels=extra_condition_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
-    control_model = controlnet_load_state_dict(control_model, sd)
-    latent_format = comfy.latent_formats.Wan21()
-    extra_conds = []
-    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
-    return control
-
 def convert_mistoline(sd):
     return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
 
@@ -718,11 +654,8 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}):
                 return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format
             else:
                 return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
-        elif "transformer_blocks.0.img_mlp.net.0.proj.weight" in controlnet_data:
-            return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options)
         elif "controlnet_x_embedder.weight" in controlnet_data:
             return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
-
     elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
         return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
 
@@ -872,14 +805,6 @@ class T2IAdapter(ControlBase):
         self.copy_to(c)
         return c
 
-    def deepclone_multigpu(self, load_device, autoregister=False):
-        c = self.copy()
-        c.t2i_model = copy.deepcopy(c.t2i_model)
-        c.device = load_device
-        if autoregister:
-            self.multigpu_clones[load_device] = c
-        return c
-
 def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
     compression_ratio = 8
     upscale_algorithm = 'nearest-exact'

comfy/gligen.py

@@ -1,10 +1,55 @@
 import math
 import torch
 from torch import nn
-from .ldm.modules.attention import CrossAttention, FeedForward
+from .ldm.modules.attention import CrossAttention
+from inspect import isfunction
 import comfy.ops
 ops = comfy.ops.manual_cast
 
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = ops.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * torch.nn.functional.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            ops.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            ops.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
 
 class GatedCrossAttentionDense(nn.Module):
     def __init__(self, query_dim, context_dim, n_heads, d_head):

comfy/image_encoders/dino2.py

@@ -31,20 +31,6 @@ class LayerScale(torch.nn.Module):
     def forward(self, x):
         return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype)
 
-class Dinov2MLP(torch.nn.Module):
-    def __init__(self, hidden_size: int, dtype, device, operations):
-        super().__init__()
-
-        mlp_ratio = 4
-        hidden_features = int(hidden_size * mlp_ratio)
-        self.fc1 = operations.Linear(hidden_size, hidden_features, bias = True, device=device, dtype=dtype)
-        self.fc2 = operations.Linear(hidden_features, hidden_size, bias = True, device=device, dtype=dtype)
-
-    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
-        hidden_state = self.fc1(hidden_state)
-        hidden_state = torch.nn.functional.gelu(hidden_state)
-        hidden_state = self.fc2(hidden_state)
-        return hidden_state
 
 class SwiGLUFFN(torch.nn.Module):
     def __init__(self, dim, dtype, device, operations):
@@ -64,15 +50,12 @@ class SwiGLUFFN(torch.nn.Module):
 
 
 class Dino2Block(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations):
         super().__init__()
         self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
         self.layer_scale1 = LayerScale(dim, dtype, device, operations)
         self.layer_scale2 = LayerScale(dim, dtype, device, operations)
-        if use_swiglu_ffn:
-            self.mlp = SwiGLUFFN(dim, dtype, device, operations)
-        else:
-            self.mlp = Dinov2MLP(dim, dtype, device, operations)
+        self.mlp = SwiGLUFFN(dim, dtype, device, operations)
         self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
         self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
@@ -83,10 +66,9 @@ class Dino2Block(torch.nn.Module):
 
 
 class Dino2Encoder(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations):
         super().__init__()
-        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
-                                          for _ in range(num_layers)])
+        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)])
 
     def forward(self, x, intermediate_output=None):
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
@@ -96,8 +78,8 @@ class Dino2Encoder(torch.nn.Module):
                 intermediate_output = len(self.layer) + intermediate_output
 
         intermediate = None
-        for i, layer in enumerate(self.layer):
-            x = layer(x, optimized_attention)
+        for i, l in enumerate(self.layer):
+            x = l(x, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
         return x, intermediate
@@ -146,10 +128,9 @@ class Dinov2Model(torch.nn.Module):
         dim = config_dict["hidden_size"]
         heads = config_dict["num_attention_heads"]
         layer_norm_eps = config_dict["layer_norm_eps"]
-        use_swiglu_ffn = config_dict["use_swiglu_ffn"]
 
         self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
-        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
+        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations)
         self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
     def forward(self, pixel_values, attention_mask=None, intermediate_output=None):

comfy/image_encoders/dino2_large.json

@@ -1,22 +0,0 @@
-{
-  "hidden_size": 1024,
-  "use_mask_token": true,
-  "patch_size": 14,
-  "image_size": 518,
-  "num_channels": 3,
-  "num_attention_heads": 16,
-  "initializer_range": 0.02,
-  "attention_probs_dropout_prob": 0.0,
-  "hidden_dropout_prob": 0.0,
-  "hidden_act": "gelu",
-  "mlp_ratio": 4,
-  "model_type": "dinov2",
-  "num_hidden_layers": 24,
-  "layer_norm_eps": 1e-6,
-  "qkv_bias": true,
-  "use_swiglu_ffn": false,
-  "layerscale_value": 1.0,
-  "drop_path_rate": 0.0,
-  "image_mean": [0.485, 0.456, 0.406],
-  "image_std": [0.229, 0.224, 0.225]
-}

comfy/k_diffusion/sa_solver.py

@@ -1,121 +0,0 @@
-# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019)
-# Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf
-# Codebase ref: https://github.com/scxue/SA-Solver
-
-import math
-from typing import Union, Callable
-import torch
-
-
-def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor:
-    """Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts.
-
-    Integral of exp((1 + tau^2) * x) * x^p dx
-        = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx,
-    with base case p=0 where integral equals product_terms[0].
-
-    where
-        product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2).
-
-    Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1).
-    Return coefficients used by the SA-Solver in data prediction mode.
-
-    Args:
-        s: Start time s.
-        t: End time t.
-        solver_order: Current order of the solver.
-        tau_t: Stochastic strength parameter in the SDE.
-
-    Returns:
-        Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,).
-    """
-    tau_mul = 1 + tau_t ** 2
-    h = t - s
-    p = torch.arange(solver_order, dtype=s.dtype, device=s.device)
-
-    # product_terms after factoring out exp((1 + tau^2) * t)
-    # Includes (1 + tau^2) factor from outside the integral
-    product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp())
-
-    # Lower triangular recursive coefficient matrix
-    # Accumulates recursive coefficients based on p / (1 + tau^2)
-    recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0)
-    log_factorial = (p + 1).lgamma()
-    recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0)
-    if tau_t > 0:
-        recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul))
-    signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0)
-    recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril()
-
-    return recursive_coeff_mat @ product_terms_factored
-
-
-def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor:
-    """Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details)."""
-    tau_mul = 1 + tau_t ** 2
-    h = lambda_t - lambda_s
-    alpha_t = sigma_next * lambda_t.exp()
-    if is_corrector_step:
-        # Simplified 1-step (order-2) corrector
-        b_1 = alpha_t * (0.5 * tau_mul * h)
-        b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1
-    else:
-        # Simplified 2-step predictor
-        b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s)
-        b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2
-    return torch.stack([b_2, b_1])
-
-
-def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor:
-    """Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18).
-
-    The solver order corresponds to the number of input lambdas (half-logSNR points).
-
-    Args:
-        sigma_next: Sigma at end time t.
-        curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,).
-        lambda_s: Lambda at start time s.
-        lambda_t: Lambda at end time t.
-        tau_t: Stochastic strength parameter in the SDE.
-        simple_order_2: Whether to enable the simple order-2 scheme.
-        is_corrector_step: Flag for corrector step in simple order-2 mode.
-
-    Returns:
-        b_i coefficients for the SA-Solver, shape (N,), where N is the solver order.
-    """
-    num_timesteps = curr_lambdas.shape[0]
-
-    if simple_order_2 and num_timesteps == 2:
-        return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step)
-
-    # Compute coefficients by solving a linear system from Lagrange basis interpolation
-    exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t)
-    vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T
-    lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs)
-
-    # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t)
-    # = sigma_t * exp(lambda_t) = alpha_t
-    # exp((1 + tau^2) * lambda_t) is extracted from the integral
-    alpha_t = sigma_next * lambda_t.exp()
-    return alpha_t * lagrange_integrals
-
-
-def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]:
-    """Return a function that controls the stochasticity of SA-Solver.
-
-    When eta = 0, SA-Solver runs as ODE. The official approach uses
-    time t to determine the SDE interval, while here we use sigma instead.
-
-    See:
-        https://github.com/scxue/SA-Solver/blob/main/README.md
-    """
-
-    def tau_func(sigma: Union[torch.Tensor, float]) -> float:
-        if eta <= 0:
-            return 0.0  # ODE
-
-        if isinstance(sigma, torch.Tensor):
-            sigma = sigma.item()
-        return eta if start_sigma >= sigma >= end_sigma else 0.0
-
-    return tau_func

comfy/k_diffusion/sampling.py

@@ -1,5 +1,4 @@
 import math
-from functools import partial
 
 from scipy import integrate
 import torch
@@ -9,7 +8,6 @@ from tqdm.auto import trange, tqdm
 
 from . import utils
 from . import deis
-from . import sa_solver
 import comfy.model_patcher
 import comfy.model_sampling
 
@@ -86,24 +84,24 @@ class BatchedBrownianTree:
     """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
 
     def __init__(self, x, t0, t1, seed=None, **kwargs):
-        self.cpu_tree = kwargs.pop("cpu", True)
+        self.cpu_tree = True
+        if "cpu" in kwargs:
+            self.cpu_tree = kwargs.pop("cpu")
         t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.pop('w0', None)
-        if w0 is None:
-            w0 = torch.zeros_like(x)
-        self.batched = False
+        w0 = kwargs.get('w0', torch.zeros_like(x))
         if seed is None:
-            seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),)
-        elif isinstance(seed, (tuple, list)):
-            if len(seed) != x.shape[0]:
-                raise ValueError("Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.")
-            self.batched = True
+            seed = torch.randint(0, 2 ** 63 - 1, []).item()
+        self.batched = True
+        try:
+            assert len(seed) == x.shape[0]
             w0 = w0[0]
-        else:
-            seed = (seed,)
+        except TypeError:
+            seed = [seed]
+            self.batched = False
         if self.cpu_tree:
-            t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu()
-        self.trees = tuple(torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed)
+            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
+        else:
+            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
 
     @staticmethod
     def sort(a, b):
@@ -111,10 +109,11 @@ class BatchedBrownianTree:
 
     def __call__(self, t0, t1):
         t0, t1, sign = self.sort(t0, t1)
-        device, dtype = t0.device, t0.dtype
         if self.cpu_tree:
-            t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float()
-        w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign)
+            w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
+        else:
+            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+
         return w if self.batched else w[0]
 
 
@@ -143,43 +142,6 @@ class BrownianTreeNoiseSampler:
         return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
 
 
-def sigma_to_half_log_snr(sigma, model_sampling):
-    """Convert sigma to half-logSNR log(alpha_t / sigma_t)."""
-    if isinstance(model_sampling, comfy.model_sampling.CONST):
-        # log((1 - t) / t) = log((1 - sigma) / sigma)
-        return sigma.logit().neg()
-    return sigma.log().neg()
-
-
-def half_log_snr_to_sigma(half_log_snr, model_sampling):
-    """Convert half-logSNR log(alpha_t / sigma_t) to sigma."""
-    if isinstance(model_sampling, comfy.model_sampling.CONST):
-        # 1 / (1 + exp(half_log_snr))
-        return half_log_snr.neg().sigmoid()
-    return half_log_snr.neg().exp()
-
-
-def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
-    """Adjust the first sigma to avoid invalid logSNR."""
-    if len(sigmas) <= 1:
-        return sigmas
-    if isinstance(model_sampling, comfy.model_sampling.CONST):
-        if sigmas[0] >= 1:
-            sigmas = sigmas.clone()
-            sigmas[0] = model_sampling.percent_to_sigma(percent_offset)
-    return sigmas
-
-
-def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor:
-    """Compute the result of h*phi_1(h) in exponential integrator methods."""
-    return torch.expm1(h)
-
-
-def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor:
-    """Compute the result of h*phi_2(h) in exponential integrator methods."""
-    return (torch.expm1(h) - h) / h
-
-
 @torch.no_grad()
 def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
     """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
@@ -422,13 +384,9 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o
             ds.pop(0)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
-            cur_order = min(i + 1, order)
-            coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
-            x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
+        cur_order = min(i + 1, order)
+        coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
+        x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
     return x
 
 
@@ -724,7 +682,6 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non
         # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
     return x
 
-
 @torch.no_grad()
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     """DPM-Solver++ (stochastic)."""
@@ -736,49 +693,38 @@ def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=N
     seed = extra_args.get("seed", None)
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
-    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
         if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
+            # Euler method
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigmas[i + 1] - sigmas[i]
+            x = x + d * dt
         else:
             # DPM-Solver++
-            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-            h = lambda_t - lambda_s
-            lambda_s_1 = lambda_s + r * h
+            t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+            h = t_next - t
+            s = t + h * r
             fac = 1 / (2 * r)
 
-            sigma_s_1 = sigma_fn(lambda_s_1)
-
-            alpha_s = sigmas[i] * lambda_s.exp()
-            alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
-            alpha_t = sigmas[i + 1] * lambda_t.exp()
-
             # Step 1
-            sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta)
-            lambda_s_1_ = sd.log().neg()
-            h_ = lambda_s_1_ - lambda_s
-            x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised
-            if eta > 0 and s_noise > 0:
-                x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su
-            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
+            s_ = t_fn(sd)
+            x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
+            x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
+            denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
 
             # Step 2
-            sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta)
-            lambda_t_ = sd.log().neg()
-            h_ = lambda_t_ - lambda_s
+            sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
+            t_next_ = t_fn(sd)
             denoised_d = (1 - fac) * denoised + fac * denoised_2
-            x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d
-            if eta > 0 and s_noise > 0:
-                x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su
+            x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
+            x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
     return x
 
 
@@ -807,7 +753,6 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
         old_denoised = denoised
     return x
 
-
 @torch.no_grad()
 def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     """DPM-Solver++(2M) SDE."""
@@ -823,12 +768,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-
     old_denoised = None
-    h, h_last = None, None
+    h_last = None
+    h = None
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -839,34 +781,26 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
             x = denoised
         else:
             # DPM-Solver++(2M) SDE
-            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-            h = lambda_t - lambda_s
-            h_eta = h * (eta + 1)
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
+            eta_h = eta * h
 
-            alpha_t = sigmas[i + 1] * lambda_t.exp()
-
-            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
+            x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
 
             if old_denoised is not None:
                 r = h_last / h
                 if solver_type == 'heun':
-                    x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)
+                    x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
                 elif solver_type == 'midpoint':
-                    x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)
+                    x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
 
-            if eta > 0 and s_noise > 0:
-                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+            if eta:
+                x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
 
         old_denoised = denoised
         h_last = h
     return x
 
-
-@torch.no_grad()
-def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
-    return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
-
-
 @torch.no_grad()
 def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """DPM-Solver++(3M) SDE."""
@@ -880,10 +814,6 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-
     denoised_1, denoised_2 = None, None
     h, h_1, h_2 = None, None, None
 
@@ -895,16 +825,13 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
             # Denoising step
             x = denoised
         else:
-            lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-            h = lambda_t - lambda_s
+            t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = s - t
             h_eta = h * (eta + 1)
 
-            alpha_t = sigmas[i + 1] * lambda_t.exp()
-
-            x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
+            x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
 
             if h_2 is not None:
-                # DPM-Solver++(3M) SDE
                 r0 = h_1 / h
                 r1 = h_2 / h
                 d1_0 = (denoised - denoised_1) / r0
@@ -913,22 +840,20 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
                 d2 = (d1_0 - d1_1) / (r0 + r1)
                 phi_2 = h_eta.neg().expm1() / h_eta + 1
                 phi_3 = phi_2 / h_eta - 0.5
-                x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2
+                x = x + phi_2 * d1 - phi_3 * d2
             elif h_1 is not None:
-                # DPM-Solver++(2M) SDE
                 r = h_1 / h
                 d = (denoised - denoised_1) / r
                 phi_2 = h_eta.neg().expm1() / h_eta + 1
-                x = x + (alpha_t * phi_2) * d
+                x = x + phi_2 * d
 
-            if eta > 0 and s_noise > 0:
+            if eta:
                 x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
 
         denoised_1, denoised_2 = denoised, denoised_1
         h_1, h_2 = h, h_1
     return x
 
-
 @torch.no_grad()
 def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     if len(sigmas) <= 1:
@@ -938,17 +863,6 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
 
-
-@torch.no_grad()
-def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
-    if len(sigmas) <= 1:
-        return x
-    extra_args = {} if extra_args is None else extra_args
-    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-    noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
-    return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
-
-
 @torch.no_grad()
 def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     if len(sigmas) <= 1:
@@ -958,7 +872,6 @@ def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
 
-
 @torch.no_grad()
 def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     if len(sigmas) <= 1:
@@ -1096,9 +1009,7 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
         d_cur = (x_cur - denoised) / t_cur
 
         order = min(max_order, i+1)
-        if t_next == 0:     # Denoising step
-            x_next = denoised
-        elif order == 1:    # First Euler step.
+        if order == 1:      # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
         elif order == 2:    # Use one history point.
             x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
@@ -1116,7 +1027,6 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
     return x_next
 
-
 #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
 #under Apache 2 license
 def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
@@ -1140,9 +1050,7 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
         d_cur = (x_cur - denoised) / t_cur
 
         order = min(max_order, i+1)
-        if t_next == 0:     # Denoising step
-            x_next = denoised
-        elif order == 1:    # First Euler step.
+        if order == 1:      # First Euler step.
             x_next = x_cur + (t_next - t_cur) * d_cur
         elif order == 2:    # Use one history point.
             h_n = (t_next - t_cur)
@@ -1182,7 +1090,6 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
 
     return x_next
 
-
 #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
 #under Apache 2 license
 @torch.no_grad()
@@ -1233,22 +1140,39 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
     return x_next
 
+@torch.no_grad()
+def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, temp[0])
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        # Euler method
+        x = denoised + d * sigmas[i + 1]
+    return x
 
 @torch.no_grad()
 def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
-    """Ancestral sampling with Euler method steps (CFG++)."""
+    """Ancestral sampling with Euler method steps."""
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
 
-    model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
-    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-
-    uncond_denoised = None
-
+    temp = [0]
     def post_cfg_function(args):
-        nonlocal uncond_denoised
-        uncond_denoised = args["uncond_denoised"]
+        temp[0] = args["uncond_denoised"]
         return args["denoised"]
 
     model_options = extra_args.get("model_options", {}).copy()
@@ -1257,33 +1181,15 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
-            alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp()
-            alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp()
-            d = to_d(x, sigmas[i], alpha_s * uncond_denoised)   # to noise
-
-            # DDIM stochastic sampling
-            sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta)
-            sigma_down = alpha_t * sigma_down
-
-            # Euler method
-            x = alpha_t * denoised + sigma_down * d
-            if eta > 0 and s_noise > 0:
-                x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+        d = to_d(x, sigmas[i], temp[0])
+        # Euler method
+        x = denoised + d * sigma_down
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
-
-
-@torch.no_grad()
-def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    """Euler method steps (CFG++)."""
-    return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None)
-
-
 @torch.no_grad()
 def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
@@ -1440,7 +1346,6 @@ def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=N
 def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
 
-
 @torch.no_grad()
 def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):
     """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
@@ -1467,32 +1372,31 @@ def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None,
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
         dt = sigmas[i + 1] - sigmas[i]
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
+        if i == 0:
             # Euler method
             if cfg_pp:
                 x = denoised + d * sigmas[i + 1]
             else:
                 x = x + d * dt
-
-            if i >= 1:
-                # Gradient estimation
+        else:
+            # Gradient estimation
+            if cfg_pp:
                 d_bar = (ge_gamma - 1) * (d - old_d)
+                x = denoised + d * sigmas[i + 1] + d_bar * dt
+            else:
+                d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
                 x = x + d_bar * dt
         old_d = d
     return x
 
-
 @torch.no_grad()
 def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
     return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True)
 
-
 @torch.no_grad()
-def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3):
-    """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169.
+def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, noise_scaler=None, max_stage=3):
+    """
+    Extended Reverse-Time SDE solver (VE ER-SDE-Solver-3). Arxiv: https://arxiv.org/abs/2309.06169.
     Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.
     """
     extra_args = {} if extra_args is None else extra_args
@@ -1500,18 +1404,12 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
-    def default_er_sde_noise_scaler(x):
-        return x * ((x ** 0.3).exp() + 10.0)
-
-    noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler
+    def default_noise_scaler(sigma):
+        return sigma * ((sigma ** 0.3).exp() + 10.0)
+    noise_scaler = default_noise_scaler if noise_scaler is None else noise_scaler
     num_integration_points = 200.0
     point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)
 
-    model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling)
-    er_lambdas = half_log_snrs.neg().exp()  # er_lambda_t = sigma_t / alpha_t
-
     old_denoised = None
     old_denoised_d = None
 
@@ -1522,265 +1420,129 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
         stage_used = min(max_stage, i + 1)
         if sigmas[i + 1] == 0:
             x = denoised
+        elif stage_used == 1:
+            r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
+            x = r * x + (1 - r) * denoised
         else:
-            er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1]
-            alpha_s = sigmas[i] / er_lambda_s
-            alpha_t = sigmas[i + 1] / er_lambda_t
-            r_alpha = alpha_t / alpha_s
-            r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s)
+            r = noise_scaler(sigmas[i + 1]) / noise_scaler(sigmas[i])
+            x = r * x + (1 - r) * denoised
 
-            # Stage 1 Euler
-            x = r_alpha * r * x + alpha_t * (1 - r) * denoised
+            dt = sigmas[i + 1] - sigmas[i]
+            sigma_step_size = -dt / num_integration_points
+            sigma_pos = sigmas[i + 1] + point_indice * sigma_step_size
+            scaled_pos = noise_scaler(sigma_pos)
 
-            if stage_used >= 2:
-                dt = er_lambda_t - er_lambda_s
-                lambda_step_size = -dt / num_integration_points
-                lambda_pos = er_lambda_t + point_indice * lambda_step_size
-                scaled_pos = noise_scaler(lambda_pos)
+            # Stage 2
+            s = torch.sum(1 / scaled_pos) * sigma_step_size
+            denoised_d = (denoised - old_denoised) / (sigmas[i] - sigmas[i - 1])
+            x = x + (dt + s * noise_scaler(sigmas[i + 1])) * denoised_d
 
-                # Stage 2
-                s = torch.sum(1 / scaled_pos) * lambda_step_size
-                denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1])
-                x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d
+            if stage_used >= 3:
+                # Stage 3
+                s_u = torch.sum((sigma_pos - sigmas[i]) / scaled_pos) * sigma_step_size
+                denoised_u = (denoised_d - old_denoised_d) / ((sigmas[i] - sigmas[i - 2]) / 2)
+                x = x + ((dt ** 2) / 2 + s_u * noise_scaler(sigmas[i + 1])) * denoised_u
+            old_denoised_d = denoised_d
 
-                if stage_used >= 3:
-                    # Stage 3
-                    s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size
-                    denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2)
-                    x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u
-                old_denoised_d = denoised_d
-
-            if s_noise > 0:
-                x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
+        if s_noise != 0 and sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
         old_denoised = denoised
     return x
 
-
 @torch.no_grad()
 def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
-    """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
-    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
-    """
+    '''
+    SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 2
+    Arxiv: https://arxiv.org/abs/2305.14267
+    '''
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
+
     inject_noise = eta > 0 and s_noise > 0
 
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
-    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-
-    fac = 1 / (2 * r)
-
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-
         if sigmas[i + 1] == 0:
             x = denoised
-            continue
+        else:
+            t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = t_next - t
+            h_eta = h * (eta + 1)
+            s = t + r * h
+            fac = 1 / (2 * r)
+            sigma_s = s.neg().exp()
 
-        lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-        h = lambda_t - lambda_s
-        h_eta = h * (eta + 1)
-        lambda_s_1 = torch.lerp(lambda_s, lambda_t, r)
-        sigma_s_1 = sigma_fn(lambda_s_1)
+            coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1()
+            if inject_noise:
+                noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt()
+                noise_coeff_2 = ((-2 * r * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
+                noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s), noise_sampler(sigma_s, sigmas[i + 1])
 
-        alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
-        alpha_t = sigmas[i + 1] * lambda_t.exp()
+            # Step 1
+            x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
+            if inject_noise:
+                x_2 = x_2 + sigma_s * (noise_coeff_1 * noise_1) * s_noise
+            denoised_2 = model(x_2, sigma_s * s_in, **extra_args)
 
-        # Step 1
-        x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised
-        if inject_noise:
-            sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)
-            x_2 = x_2 + sde_noise * sigma_s_1 * s_noise
-        denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
-
-        # Step 2
-        denoised_d = torch.lerp(denoised, denoised_2, fac)
-        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
-        if inject_noise:
-            segment_factor = (r - 1) * h * eta
-            sde_noise = sde_noise * segment_factor.exp()
-            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1])
-            x = x + sde_noise * sigmas[i + 1] * s_noise
+            # Step 2
+            denoised_d = (1 - fac) * denoised + fac * denoised_2
+            x = (coeff_2 + 1) * x - coeff_2 * denoised_d
+            if inject_noise:
+                x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
     return x
 
-
 @torch.no_grad()
 def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
-    """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3.
-    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
-    """
+    '''
+    SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 3
+    Arxiv: https://arxiv.org/abs/2305.14267
+    '''
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
-    inject_noise = eta > 0 and s_noise > 0
 
-    model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
-    sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
-    lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+    inject_noise = eta > 0 and s_noise > 0
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-
         if sigmas[i + 1] == 0:
             x = denoised
-            continue
+        else:
+            t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
+            h = t_next - t
+            h_eta = h * (eta + 1)
+            s_1 = t + r_1 * h
+            s_2 = t + r_2 * h
+            sigma_s_1, sigma_s_2 = s_1.neg().exp(), s_2.neg().exp()
 
-        lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
-        h = lambda_t - lambda_s
-        h_eta = h * (eta + 1)
-        lambda_s_1 = torch.lerp(lambda_s, lambda_t, r_1)
-        lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2)
-        sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
+            coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
+            if inject_noise:
+                noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
+                noise_coeff_2 = ((-2 * r_1 * h * eta).expm1() - (-2 * r_2 * h * eta).expm1()).sqrt()
+                noise_coeff_3 = ((-2 * r_2 * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
+                noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
 
-        alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
-        alpha_s_2 = sigma_s_2 * lambda_s_2.exp()
-        alpha_t = sigmas[i + 1] * lambda_t.exp()
+            # Step 1
+            x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
+            if inject_noise:
+                x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
+            denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
 
-        # Step 1
-        x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised
-        if inject_noise:
-            sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)
-            x_2 = x_2 + sde_noise * sigma_s_1 * s_noise
-        denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+            # Step 2
+            x_3 = (coeff_2 + 1) * x - coeff_2 * denoised + (r_2 / r_1) * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
+            if inject_noise:
+                x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
+            denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
 
-        # Step 2
-        a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta)
-        a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2
-        x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2)
-        if inject_noise:
-            segment_factor = (r_1 - r_2) * h * eta
-            sde_noise = sde_noise * segment_factor.exp()
-            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2)
-            x_3 = x_3 + sde_noise * sigma_s_2 * s_noise
-        denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
-
-        # Step 3
-        b3 = ei_h_phi_2(-h_eta) / r_2
-        b1 = ei_h_phi_1(-h_eta) - b3
-        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3)
-        if inject_noise:
-            segment_factor = (r_2 - 1) * h * eta
-            sde_noise = sde_noise * segment_factor.exp()
-            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1])
-            x = x + sde_noise * sigmas[i + 1] * s_noise
+            # Step 3
+            x = (coeff_3 + 1) * x - coeff_3 * denoised + (1. / r_2) * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
+            if inject_noise:
+                x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise
     return x
-
-
-@torch.no_grad()
-def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False):
-    """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023)."""
-    if len(sigmas) <= 1:
-        return x
-    extra_args = {} if extra_args is None else extra_args
-    seed = extra_args.get("seed", None)
-    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
-    s_in = x.new_ones([x.shape[0]])
-
-    model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
-    sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
-    lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
-
-    if tau_func is None:
-        # Use default interval for stochastic sampling
-        start_sigma = model_sampling.percent_to_sigma(0.2)
-        end_sigma = model_sampling.percent_to_sigma(0.8)
-        tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0)
-
-    max_used_order = max(predictor_order, corrector_order)
-    x_pred = x  # x: current state, x_pred: predicted next state
-
-    h = 0.0
-    tau_t = 0.0
-    noise = 0.0
-    pred_list = []
-
-    # Lower order near the end to improve stability
-    lower_order_to_end = sigmas[-1].item() == 0
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        # Evaluation
-        denoised = model(x_pred, sigmas[i] * s_in, **extra_args)
-        if callback is not None:
-            callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
-        pred_list.append(denoised)
-        pred_list = pred_list[-max_used_order:]
-
-        predictor_order_used = min(predictor_order, len(pred_list))
-        if i == 0 or (sigmas[i + 1] == 0 and not use_pece):
-            corrector_order_used = 0
-        else:
-            corrector_order_used = min(corrector_order, len(pred_list))
-
-        if lower_order_to_end:
-            predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i)
-            corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i)
-
-        # Corrector
-        if corrector_order_used == 0:
-            # Update by the predicted state
-            x = x_pred
-        else:
-            curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1]
-            b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
-                sigmas[i],
-                curr_lambdas,
-                lambdas[i - 1],
-                lambdas[i],
-                tau_t,
-                simple_order_2,
-                is_corrector_step=True,
-            )
-            pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1)    # (B, K, ...)
-            corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0]))  # (B, ...)
-            x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res
-
-            if tau_t > 0 and s_noise > 0:
-                # The noise from the previous predictor step
-                x = x + noise
-
-            if use_pece:
-                # Evaluate the corrected state
-                denoised = model(x, sigmas[i] * s_in, **extra_args)
-                pred_list[-1] = denoised
-
-        # Predictor
-        if sigmas[i + 1] == 0:
-            # Denoising step
-            x = denoised
-        else:
-            tau_t = tau_func(sigmas[i + 1])
-            curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
-            b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
-                sigmas[i + 1],
-                curr_lambdas,
-                lambdas[i],
-                lambdas[i + 1],
-                tau_t,
-                simple_order_2,
-                is_corrector_step=False,
-            )
-            pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1)    # (B, K, ...)
-            pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0]))  # (B, ...)
-            h = lambdas[i + 1] - lambdas[i]
-            x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res
-
-            if tau_t > 0 and s_noise > 0:
-                noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
-                x_pred = x_pred + noise
-    return x
-
-
-@torch.no_grad()
-def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
-    """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
-    return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)

comfy/latent_formats.py

@@ -457,170 +457,11 @@ class Wan21(LatentFormat):
         latents_std = self.latents_std.to(latent.device, latent.dtype)
         return latent * latents_std / self.scale_factor + latents_mean
 
-class Wan22(Wan21):
-    latent_channels = 48
-    latent_dimensions = 3
-
-    latent_rgb_factors = [
-            [ 0.0119,  0.0103,  0.0046],
-            [-0.1062, -0.0504,  0.0165],
-            [ 0.0140,  0.0409,  0.0491],
-            [-0.0813, -0.0677,  0.0607],
-            [ 0.0656,  0.0851,  0.0808],
-            [ 0.0264,  0.0463,  0.0912],
-            [ 0.0295,  0.0326,  0.0590],
-            [-0.0244, -0.0270,  0.0025],
-            [ 0.0443, -0.0102,  0.0288],
-            [-0.0465, -0.0090, -0.0205],
-            [ 0.0359,  0.0236,  0.0082],
-            [-0.0776,  0.0854,  0.1048],
-            [ 0.0564,  0.0264,  0.0561],
-            [ 0.0006,  0.0594,  0.0418],
-            [-0.0319, -0.0542, -0.0637],
-            [-0.0268,  0.0024,  0.0260],
-            [ 0.0539,  0.0265,  0.0358],
-            [-0.0359, -0.0312, -0.0287],
-            [-0.0285, -0.1032, -0.1237],
-            [ 0.1041,  0.0537,  0.0622],
-            [-0.0086, -0.0374, -0.0051],
-            [ 0.0390,  0.0670,  0.2863],
-            [ 0.0069,  0.0144,  0.0082],
-            [ 0.0006, -0.0167,  0.0079],
-            [ 0.0313, -0.0574, -0.0232],
-            [-0.1454, -0.0902, -0.0481],
-            [ 0.0714,  0.0827,  0.0447],
-            [-0.0304, -0.0574, -0.0196],
-            [ 0.0401,  0.0384,  0.0204],
-            [-0.0758, -0.0297, -0.0014],
-            [ 0.0568,  0.1307,  0.1372],
-            [-0.0055, -0.0310, -0.0380],
-            [ 0.0239, -0.0305,  0.0325],
-            [-0.0663, -0.0673, -0.0140],
-            [-0.0416, -0.0047, -0.0023],
-            [ 0.0166,  0.0112, -0.0093],
-            [-0.0211,  0.0011,  0.0331],
-            [ 0.1833,  0.1466,  0.2250],
-            [-0.0368,  0.0370,  0.0295],
-            [-0.3441, -0.3543, -0.2008],
-            [-0.0479, -0.0489, -0.0420],
-            [-0.0660, -0.0153,  0.0800],
-            [-0.0101,  0.0068,  0.0156],
-            [-0.0690, -0.0452, -0.0927],
-            [-0.0145,  0.0041,  0.0015],
-            [ 0.0421,  0.0451,  0.0373],
-            [ 0.0504, -0.0483, -0.0356],
-            [-0.0837,  0.0168,  0.0055]
-        ]
-
-    latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388]
-
-    def __init__(self):
-        self.scale_factor = 1.0
-        self.latents_mean = torch.tensor([
-                -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
-                -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
-                -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502,
-                -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230,
-                -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748,
-                0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667,
-            ]).view(1, self.latent_channels, 1, 1, 1)
-        self.latents_std = torch.tensor([
-                0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,
-                0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,
-                0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,
-                0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,
-                0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,
-                0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
-            ]).view(1, self.latent_channels, 1, 1, 1)
-
-class HunyuanImage21(LatentFormat):
-    latent_channels = 64
-    latent_dimensions = 2
-    scale_factor = 0.75289
-
-    latent_rgb_factors = [
-        [-0.0154, -0.0397, -0.0521],
-        [ 0.0005,  0.0093,  0.0006],
-        [-0.0805, -0.0773, -0.0586],
-        [-0.0494, -0.0487, -0.0498],
-        [-0.0212, -0.0076, -0.0261],
-        [-0.0179, -0.0417, -0.0505],
-        [ 0.0158,  0.0310,  0.0239],
-        [ 0.0409,  0.0516,  0.0201],
-        [ 0.0350,  0.0553,  0.0036],
-        [-0.0447, -0.0327, -0.0479],
-        [-0.0038, -0.0221, -0.0365],
-        [-0.0423, -0.0718, -0.0654],
-        [ 0.0039,  0.0368,  0.0104],
-        [ 0.0655,  0.0217,  0.0122],
-        [ 0.0490,  0.1638,  0.2053],
-        [ 0.0932,  0.0829,  0.0650],
-        [-0.0186, -0.0209, -0.0135],
-        [-0.0080, -0.0076, -0.0148],
-        [-0.0284, -0.0201,  0.0011],
-        [-0.0642, -0.0294, -0.0777],
-        [-0.0035,  0.0076, -0.0140],
-        [ 0.0519,  0.0731,  0.0887],
-        [-0.0102,  0.0095,  0.0704],
-        [ 0.0068,  0.0218, -0.0023],
-        [-0.0726, -0.0486, -0.0519],
-        [ 0.0260,  0.0295,  0.0263],
-        [ 0.0250,  0.0333,  0.0341],
-        [ 0.0168, -0.0120, -0.0174],
-        [ 0.0226,  0.1037,  0.0114],
-        [ 0.2577,  0.1906,  0.1604],
-        [-0.0646, -0.0137, -0.0018],
-        [-0.0112,  0.0309,  0.0358],
-        [-0.0347,  0.0146, -0.0481],
-        [ 0.0234,  0.0179,  0.0201],
-        [ 0.0157,  0.0313,  0.0225],
-        [ 0.0423,  0.0675,  0.0524],
-        [-0.0031,  0.0027, -0.0255],
-        [ 0.0447,  0.0555,  0.0330],
-        [-0.0152,  0.0103,  0.0299],
-        [-0.0755, -0.0489, -0.0635],
-        [ 0.0853,  0.0788,  0.1017],
-        [-0.0272, -0.0294, -0.0471],
-        [ 0.0440,  0.0400, -0.0137],
-        [ 0.0335,  0.0317, -0.0036],
-        [-0.0344, -0.0621, -0.0984],
-        [-0.0127, -0.0630, -0.0620],
-        [-0.0648,  0.0360,  0.0924],
-        [-0.0781, -0.0801, -0.0409],
-        [ 0.0363,  0.0613,  0.0499],
-        [ 0.0238,  0.0034,  0.0041],
-        [-0.0135,  0.0258,  0.0310],
-        [ 0.0614,  0.1086,  0.0589],
-        [ 0.0428,  0.0350,  0.0205],
-        [ 0.0153,  0.0173, -0.0018],
-        [-0.0288, -0.0455, -0.0091],
-        [ 0.0344,  0.0109, -0.0157],
-        [-0.0205, -0.0247, -0.0187],
-        [ 0.0487,  0.0126,  0.0064],
-        [-0.0220, -0.0013,  0.0074],
-        [-0.0203, -0.0094, -0.0048],
-        [-0.0719,  0.0429, -0.0442],
-        [ 0.1042,  0.0497,  0.0356],
-        [-0.0659, -0.0578, -0.0280],
-        [-0.0060, -0.0322, -0.0234]]
-
-    latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206]
-
-class HunyuanImage21Refiner(LatentFormat):
-    latent_channels = 64
-    latent_dimensions = 3
-    scale_factor = 1.03682
-
 class Hunyuan3Dv2(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
     scale_factor = 0.9990943042622529
 
-class Hunyuan3Dv2_1(LatentFormat):
-    scale_factor = 1.0039506158752403
-    latent_channels = 64
-    latent_dimensions = 1
-
 class Hunyuan3Dv2mini(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1
@@ -629,20 +470,3 @@ class Hunyuan3Dv2mini(LatentFormat):
 class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
-
-class ChromaRadiance(LatentFormat):
-    latent_channels = 3
-
-    def __init__(self):
-        self.latent_rgb_factors = [
-            # R    G    B
-            [ 1.0, 0.0, 0.0 ],
-            [ 0.0, 1.0, 0.0 ],
-            [ 0.0, 0.0, 1.0 ]
-        ]
-
-    def process_in(self, latent):
-        return latent
-
-    def process_out(self, latent):
-        return latent

comfy/ldm/ace/attention.py

@@ -133,7 +133,6 @@ class Attention(nn.Module):
         hidden_states: torch.Tensor,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        transformer_options={},
         **cross_attention_kwargs,
     ) -> torch.Tensor:
         return self.processor(
@@ -141,7 +140,6 @@ class Attention(nn.Module):
             hidden_states,
             encoder_hidden_states=encoder_hidden_states,
             attention_mask=attention_mask,
-            transformer_options=transformer_options,
             **cross_attention_kwargs,
         )
 
@@ -368,7 +366,6 @@ class CustomerAttnProcessor2_0:
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
         rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
-        transformer_options={},
         *args,
         **kwargs,
     ) -> torch.Tensor:
@@ -436,7 +433,7 @@ class CustomerAttnProcessor2_0:
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
         hidden_states = optimized_attention(
-            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options,
+            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True,
         ).to(query.dtype)
 
         # linear proj
@@ -700,7 +697,6 @@ class LinearTransformerBlock(nn.Module):
         rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         temb: torch.FloatTensor = None,
-        transformer_options={},
     ):
 
         N = hidden_states.shape[0]
@@ -724,7 +720,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=encoder_attention_mask,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=rotary_freqs_cis_cross,
-                transformer_options=transformer_options,
             )
         else:
             attn_output, _ = self.attn(
@@ -734,7 +729,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=None,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=None,
-                transformer_options=transformer_options,
             )
 
         if self.use_adaln_single:
@@ -749,7 +743,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=encoder_attention_mask,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=rotary_freqs_cis_cross,
-                transformer_options=transformer_options,
             )
             hidden_states = attn_output + hidden_states
 

comfy/ldm/ace/model.py

@@ -19,7 +19,6 @@ import torch
 from torch import nn
 
 import comfy.model_management
-import comfy.patcher_extension
 
 from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
 from .attention import LinearTransformerBlock, t2i_modulate
@@ -314,7 +313,6 @@ class ACEStepTransformer2DModel(nn.Module):
         output_length: int = 0,
         block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,
         controlnet_scale: Union[float, torch.Tensor] = 1.0,
-        transformer_options={},
     ):
         embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype))
         temb = self.t_block(embedded_timestep)
@@ -340,34 +338,12 @@ class ACEStepTransformer2DModel(nn.Module):
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=encoder_rotary_freqs_cis,
                 temb=temb,
-                transformer_options=transformer_options,
             )
 
         output = self.final_layer(hidden_states, embedded_timestep, output_length)
         return output
 
-    def forward(self,
-        x,
-        timestep,
-        attention_mask=None,
-        context: Optional[torch.Tensor] = None,
-        text_attention_mask: Optional[torch.LongTensor] = None,
-        speaker_embeds: Optional[torch.FloatTensor] = None,
-        lyric_token_idx: Optional[torch.LongTensor] = None,
-        lyric_mask: Optional[torch.LongTensor] = None,
-        block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,
-        controlnet_scale: Union[float, torch.Tensor] = 1.0,
-        lyrics_strength=1.0,
-        **kwargs
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x, timestep, attention_mask, context, text_attention_mask, speaker_embeds, lyric_token_idx, lyric_mask, block_controlnet_hidden_states,
-                  controlnet_scale, lyrics_strength, **kwargs)
-
-    def _forward(
+    def forward(
         self,
         x,
         timestep,
@@ -395,7 +371,6 @@ class ACEStepTransformer2DModel(nn.Module):
 
         output_length = hidden_states.shape[-1]
 
-        transformer_options = kwargs.get("transformer_options", {})
         output = self.decode(
             hidden_states=hidden_states,
             attention_mask=attention_mask,
@@ -405,7 +380,6 @@ class ACEStepTransformer2DModel(nn.Module):
             output_length=output_length,
             block_controlnet_hidden_states=block_controlnet_hidden_states,
             controlnet_scale=controlnet_scale,
-            transformer_options=transformer_options,
         )
 
         return output

comfy/ldm/ace/vae/music_dcae_pipeline.py

@@ -23,6 +23,8 @@ class MusicDCAE(torch.nn.Module):
         else:
             self.source_sample_rate = source_sample_rate
 
+        # self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
+
         self.transform = transforms.Compose([
             transforms.Normalize(0.5, 0.5),
         ])
@@ -35,6 +37,10 @@ class MusicDCAE(torch.nn.Module):
         self.scale_factor = 0.1786
         self.shift_factor = -1.9091
 
+    def load_audio(self, audio_path):
+        audio, sr = torchaudio.load(audio_path)
+        return audio, sr
+
     def forward_mel(self, audios):
         mels = []
         for i in range(len(audios)):
@@ -67,8 +73,10 @@ class MusicDCAE(torch.nn.Module):
             latent = self.dcae.encoder(mel.unsqueeze(0))
             latents.append(latent)
         latents = torch.cat(latents, dim=0)
+        # latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long()
         latents = (latents - self.shift_factor) * self.scale_factor
         return latents
+        # return latents, latent_lengths
 
     @torch.no_grad()
     def decode(self, latents, audio_lengths=None, sr=None):
@@ -83,7 +91,9 @@ class MusicDCAE(torch.nn.Module):
             wav = self.vocoder.decode(mels[0]).squeeze(1)
 
             if sr is not None:
+                # resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype)
                 wav = torchaudio.functional.resample(wav, 44100, sr)
+                # wav = resampler(wav)
             else:
                 sr = 44100
             pred_wavs.append(wav)
@@ -91,6 +101,7 @@ class MusicDCAE(torch.nn.Module):
         if audio_lengths is not None:
             pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
         return torch.stack(pred_wavs)
+        # return sr, pred_wavs
 
     def forward(self, audios, audio_lengths=None, sr=None):
         latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)

comfy/ldm/audio/dit.py

@@ -298,8 +298,7 @@ class Attention(nn.Module):
         mask = None,
         context_mask = None,
         rotary_pos_emb = None,
-        causal = None,
-        transformer_options={},
+        causal = None
     ):
         h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
 
@@ -364,7 +363,7 @@ class Attention(nn.Module):
             heads_per_kv_head = h // kv_h
             k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
 
-        out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, h, skip_reshape=True)
         out = self.to_out(out)
 
         if mask is not None:
@@ -489,8 +488,7 @@ class TransformerBlock(nn.Module):
         global_cond=None,
         mask = None,
         context_mask = None,
-        rotary_pos_emb = None,
-        transformer_options={}
+        rotary_pos_emb = None
     ):
         if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
 
@@ -500,12 +498,12 @@ class TransformerBlock(nn.Module):
             residual = x
             x = self.pre_norm(x)
             x = x * (1 + scale_self) + shift_self
-            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
             x = x * torch.sigmoid(1 - gate_self)
             x = x + residual
 
             if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
 
             if self.conformer is not None:
                 x = x + self.conformer(x)
@@ -519,10 +517,10 @@ class TransformerBlock(nn.Module):
             x = x + residual
 
         else:
-            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
 
             if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
 
             if self.conformer is not None:
                 x = x + self.conformer(x)
@@ -608,8 +606,7 @@ class ContinuousTransformer(nn.Module):
         return_info = False,
         **kwargs
     ):
-        transformer_options = kwargs.get("transformer_options", {})
-        patches_replace = transformer_options.get("patches_replace", {})
+        patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {})
         batch, seq, device = *x.shape[:2], x.device
         context = kwargs["context"]
 
@@ -635,7 +632,7 @@ class ContinuousTransformer(nn.Module):
         # Attention layers
 
         if self.rotary_pos_emb is not None:
-            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
         else:
             rotary_pos_emb = None
 
@@ -648,13 +645,13 @@ class ContinuousTransformer(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"])
+                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context)
             # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
 
             if return_info:

comfy/ldm/aura/mmdit.py

@@ -9,7 +9,6 @@ import torch.nn.functional as F
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.ops
-import comfy.patcher_extension
 import comfy.ldm.common_dit
 
 def modulate(x, shift, scale):
@@ -85,7 +84,7 @@ class SingleAttention(nn.Module):
         )
 
     #@torch.compile()
-    def forward(self, c, transformer_options={}):
+    def forward(self, c):
 
         bsz, seqlen1, _ = c.shape
 
@@ -95,7 +94,7 @@ class SingleAttention(nn.Module):
         v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
         q, k = self.q_norm1(q), self.k_norm1(k)
 
-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
         c = self.w1o(output)
         return c
 
@@ -144,7 +143,7 @@ class DoubleAttention(nn.Module):
 
 
     #@torch.compile()
-    def forward(self, c, x, transformer_options={}):
+    def forward(self, c, x):
 
         bsz, seqlen1, _ = c.shape
         bsz, seqlen2, _ = x.shape
@@ -168,7 +167,7 @@ class DoubleAttention(nn.Module):
             torch.cat([cv, xv], dim=1),
         )
 
-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
 
         c, x = output.split([seqlen1, seqlen2], dim=1)
         c = self.w1o(c)
@@ -207,7 +206,7 @@ class MMDiTBlock(nn.Module):
         self.is_last = is_last
 
     #@torch.compile()
-    def forward(self, c, x, global_cond, transformer_options={}, **kwargs):
+    def forward(self, c, x, global_cond, **kwargs):
 
         cres, xres = c, x
 
@@ -225,7 +224,7 @@ class MMDiTBlock(nn.Module):
         x = modulate(self.normX1(x), xshift_msa, xscale_msa)
 
         # attention
-        c, x = self.attn(c, x, transformer_options=transformer_options)
+        c, x = self.attn(c, x)
 
 
         c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
@@ -255,13 +254,13 @@ class DiTBlock(nn.Module):
         self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
 
     #@torch.compile()
-    def forward(self, cx, global_cond, transformer_options={}, **kwargs):
+    def forward(self, cx, global_cond, **kwargs):
         cxres = cx
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
             global_cond
         ).chunk(6, dim=1)
         cx = modulate(self.norm1(cx), shift_msa, scale_msa)
-        cx = self.attn(cx, transformer_options=transformer_options)
+        cx = self.attn(cx)
         cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
         mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
         cx = gate_mlp.unsqueeze(1) * mlpout
@@ -437,13 +436,6 @@ class MMDiT(nn.Module):
         return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
 
     def forward(self, x, timestep, context, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, transformer_options={}, **kwargs):
         patches_replace = transformer_options.get("patches_replace", {})
         # patchify x, add PE
         b, c, h, w = x.shape
@@ -473,14 +465,13 @@ class MMDiT(nn.Module):
                         out = {}
                         out["txt"], out["img"] = layer(args["txt"],
                                                        args["img"],
-                                                       args["vec"],
-                                                       transformer_options=args["transformer_options"])
+                                                       args["vec"])
                         return out
-                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
                     c = out["txt"]
                     x = out["img"]
                 else:
-                    c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs)
+                    c, x = layer(c, x, global_cond, **kwargs)
 
         if len(self.single_layers) > 0:
             c_len = c.size(1)
@@ -489,13 +480,13 @@ class MMDiT(nn.Module):
                 if ("single_block", i) in blocks_replace:
                     def block_wrap(args):
                         out = {}
-                        out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"])
+                        out["img"] = layer(args["img"], args["vec"])
                         return out
 
-                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
                     cx = out["img"]
                 else:
-                    cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs)
+                    cx = layer(cx, global_cond, **kwargs)
 
             x = cx[:, c_len:]
 

comfy/ldm/cascade/common.py

@@ -32,12 +32,12 @@ class OptimizedAttention(nn.Module):
 
         self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
 
-    def forward(self, q, k, v, transformer_options={}):
+    def forward(self, q, k, v):
         q = self.to_q(q)
         k = self.to_k(k)
         v = self.to_v(v)
 
-        out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, self.heads)
 
         return self.out_proj(out)
 
@@ -47,13 +47,13 @@ class Attention2D(nn.Module):
         self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
         # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)
 
-    def forward(self, x, kv, self_attn=False, transformer_options={}):
+    def forward(self, x, kv, self_attn=False):
         orig_shape = x.shape
         x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1)  # Bx4xHxW -> Bx(HxW)x4
         if self_attn:
             kv = torch.cat([x, kv], dim=1)
         # x = self.attn(x, kv, kv, need_weights=False)[0]
-        x = self.attn(x, kv, kv, transformer_options=transformer_options)
+        x = self.attn(x, kv, kv)
         x = x.permute(0, 2, 1).view(*orig_shape)
         return x
 
@@ -114,9 +114,9 @@ class AttnBlock(nn.Module):
             operations.Linear(c_cond, c, dtype=dtype, device=device)
         )
 
-    def forward(self, x, kv, transformer_options={}):
+    def forward(self, x, kv):
         kv = self.kv_mapper(kv)
-        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options)
+        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
         return x
 
 

comfy/ldm/cascade/stage_b.py

@@ -173,7 +173,7 @@ class StageB(nn.Module):
         clip = self.clip_norm(clip)
         return clip
 
-    def _down_encode(self, x, r_embed, clip, transformer_options={}):
+    def _down_encode(self, x, r_embed, clip):
         level_outputs = []
         block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
         for down_block, downscaler, repmap in block_group:
@@ -187,7 +187,7 @@ class StageB(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -199,7 +199,7 @@ class StageB(nn.Module):
             level_outputs.insert(0, x)
         return level_outputs
 
-    def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}):
+    def _up_decode(self, level_outputs, r_embed, clip):
         x = level_outputs[0]
         block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
         for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -216,7 +216,7 @@ class StageB(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -228,7 +228,7 @@ class StageB(nn.Module):
             x = upscaler(x)
         return x
 
-    def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs):
+    def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
         if pixels is None:
             pixels = x.new_zeros(x.size(0), 3, 8, 8)
 
@@ -245,8 +245,8 @@ class StageB(nn.Module):
             nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
         x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
                                           align_corners=True)
-        level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options)
-        x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options)
+        level_outputs = self._down_encode(x, r_embed, clip)
+        x = self._up_decode(level_outputs, r_embed, clip)
         return self.clf(x)
 
     def update_weights_ema(self, src_model, beta=0.999):

comfy/ldm/cascade/stage_c.py

@@ -182,7 +182,7 @@ class StageC(nn.Module):
         clip = self.clip_norm(clip)
         return clip
 
-    def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}):
+    def _down_encode(self, x, r_embed, clip, cnet=None):
         level_outputs = []
         block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
         for down_block, downscaler, repmap in block_group:
@@ -201,7 +201,7 @@ class StageC(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -213,7 +213,7 @@ class StageC(nn.Module):
             level_outputs.insert(0, x)
         return level_outputs
 
-    def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}):
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
         x = level_outputs[0]
         block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
         for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -235,7 +235,7 @@ class StageC(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -247,7 +247,7 @@ class StageC(nn.Module):
             x = upscaler(x)
         return x
 
-    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
         # Process the conditioning embeddings
         r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
         for c in self.t_conds:
@@ -262,8 +262,8 @@ class StageC(nn.Module):
 
         # Model Blocks
         x = self.embedding(x)
-        level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options)
-        x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options)
+        level_outputs = self._down_encode(x, r_embed, clip, cnet)
+        x = self._up_decode(level_outputs, r_embed, clip, cnet)
         return self.clf(x)
 
     def update_weights_ema(self, src_model, beta=0.999):

comfy/ldm/chroma/layers.py

@@ -76,17 +76,19 @@ class DoubleStreamBlock(nn.Module):
         )
         self.flipped_img_txt = flipped_img_txt
 
-    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}):
+    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
         (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
 
         # prepare image for attention
-        img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img))
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
         img_qkv = self.img_attn.qkv(img_modulated)
         img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
 
         # prepare txt for attention
-        txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt))
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
         txt_qkv = self.txt_attn.qkv(txt_modulated)
         txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
@@ -95,17 +97,17 @@ class DoubleStreamBlock(nn.Module):
         attn = attention(torch.cat((txt_q, img_q), dim=2),
                          torch.cat((txt_k, img_k), dim=2),
                          torch.cat((txt_v, img_v), dim=2),
-                         pe=pe, mask=attn_mask, transformer_options=transformer_options)
+                         pe=pe, mask=attn_mask)
 
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
         # calculate the img bloks
-        img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn))
-        img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img))))
+        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
 
         # calculate the txt bloks
-        txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn))
-        txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt))))
+        txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
 
         if txt.dtype == torch.float16:
             txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
@@ -148,19 +150,19 @@ class SingleStreamBlock(nn.Module):
 
         self.mlp_act = nn.GELU(approximate="tanh")
 
-    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor:
+    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
         mod = vec
-        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
         qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         q, k = self.norm(q, k, v)
 
         # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        x.addcmul_(mod.gate, output)
+        x += mod.gate * output
         if x.dtype == torch.float16:
             x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
         return x
@@ -176,6 +178,6 @@ class LastLayer(nn.Module):
         shift, scale = vec
         shift = shift.squeeze(1)
         scale = scale.squeeze(1)
-        x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
         x = self.linear(x)
         return x

comfy/ldm/chroma/model.py

@@ -5,7 +5,6 @@ from dataclasses import dataclass
 import torch
 from torch import Tensor, nn
 from einops import rearrange, repeat
-import comfy.patcher_extension
 import comfy.ldm.common_dit
 
 from comfy.ldm.flux.layers import (
@@ -151,6 +150,8 @@ class Chroma(nn.Module):
         attn_mask: Tensor = None,
     ) -> Tensor:
         patches_replace = transformer_options.get("patches_replace", {})
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
 
         # running on sequences img
         img = self.img_in(img)
@@ -162,7 +163,7 @@ class Chroma(nn.Module):
         distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
 
         # get all modulation index
-        modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype)
+        modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype)
         # we need to broadcast the modulation index here so each batch has all of the index
         modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
         # and we need to broadcast timestep and guidance along too
@@ -191,16 +192,14 @@ class Chroma(nn.Module):
                                                        txt=args["txt"],
                                                        vec=args["vec"],
                                                        pe=args["pe"],
-                                                       attn_mask=args.get("attn_mask"),
-                                                       transformer_options=args.get("transformer_options"))
+                                                       attn_mask=args.get("attn_mask"))
                         return out
 
                     out = blocks_replace[("double_block", i)]({"img": img,
                                                                "txt": txt,
                                                                "vec": double_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask,
-                                                               "transformer_options": transformer_options},
+                                                               "attn_mask": attn_mask},
                                                               {"original_block": block_wrap})
                     txt = out["txt"]
                     img = out["img"]
@@ -209,8 +208,7 @@ class Chroma(nn.Module):
                                      txt=txt,
                                      vec=double_mod,
                                      pe=pe,
-                                     attn_mask=attn_mask,
-                                     transformer_options=transformer_options)
+                                     attn_mask=attn_mask)
 
                 if control is not None: # Controlnet
                     control_i = control.get("input")
@@ -230,19 +228,17 @@ class Chroma(nn.Module):
                         out["img"] = block(args["img"],
                                            vec=args["vec"],
                                            pe=args["pe"],
-                                           attn_mask=args.get("attn_mask"),
-                                           transformer_options=args.get("transformer_options"))
+                                           attn_mask=args.get("attn_mask"))
                         return out
 
                     out = blocks_replace[("single_block", i)]({"img": img,
                                                                "vec": single_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask,
-                                                               "transformer_options": transformer_options},
+                                                               "attn_mask": attn_mask},
                                                               {"original_block": block_wrap})
                     img = out["img"]
                 else:
-                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
 
                 if control is not None: # Controlnet
                     control_o = control.get("output")
@@ -252,29 +248,19 @@ class Chroma(nn.Module):
                             img[:, txt.shape[1] :, ...] += add
 
         img = img[:, txt.shape[1] :, ...]
-        if hasattr(self, "final_layer"):
-            final_mod = self.get_modulations(mod_vectors, "final")
-            img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
+        final_mod = self.get_modulations(mod_vectors, "final")
+        img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
     def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        patch_size = 2
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
 
-        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
 
-        if img.ndim != 3 or context.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
-        h_len = ((h + (self.patch_size // 2)) // self.patch_size)
-        w_len = ((w + (self.patch_size // 2)) // self.patch_size)
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
         img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
@@ -282,4 +268,4 @@ class Chroma(nn.Module):
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w]
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]

comfy/ldm/chroma_radiance/layers.py

@@ -1,206 +0,0 @@
-# Adapted from https://github.com/lodestone-rock/flow
-from functools import lru_cache
-
-import torch
-from torch import nn
-
-from comfy.ldm.flux.layers import RMSNorm
-
-
-class NerfEmbedder(nn.Module):
-    """
-    An embedder module that combines input features with a 2D positional
-    encoding that mimics the Discrete Cosine Transform (DCT).
-
-    This module takes an input tensor of shape (B, P^2, C), where P is the
-    patch size, and enriches it with positional information before projecting
-    it to a new hidden size.
-    """
-    def __init__(
-        self,
-        in_channels: int,
-        hidden_size_input: int,
-        max_freqs: int,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        """
-        Initializes the NerfEmbedder.
-
-        Args:
-            in_channels (int): The number of channels in the input tensor.
-            hidden_size_input (int): The desired dimension of the output embedding.
-            max_freqs (int): The number of frequency components to use for both
-                             the x and y dimensions of the positional encoding.
-                             The total number of positional features will be max_freqs^2.
-        """
-        super().__init__()
-        self.dtype = dtype
-        self.max_freqs = max_freqs
-        self.hidden_size_input = hidden_size_input
-
-        # A linear layer to project the concatenated input features and
-        # positional encodings to the final output dimension.
-        self.embedder = nn.Sequential(
-            operations.Linear(in_channels + max_freqs**2, hidden_size_input, dtype=dtype, device=device)
-        )
-
-    @lru_cache(maxsize=4)
-    def fetch_pos(self, patch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-        """
-        Generates and caches 2D DCT-like positional embeddings for a given patch size.
-
-        The LRU cache is a performance optimization that avoids recomputing the
-        same positional grid on every forward pass.
-
-        Args:
-            patch_size (int): The side length of the square input patch.
-            device: The torch device to create the tensors on.
-            dtype: The torch dtype for the tensors.
-
-        Returns:
-            A tensor of shape (1, patch_size^2, max_freqs^2) containing the
-            positional embeddings.
-        """
-        # Create normalized 1D coordinate grids from 0 to 1.
-        pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
-        pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
-
-        # Create a 2D meshgrid of coordinates.
-        pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij")
-
-        # Reshape positions to be broadcastable with frequencies.
-        # Shape becomes (patch_size^2, 1, 1).
-        pos_x = pos_x.reshape(-1, 1, 1)
-        pos_y = pos_y.reshape(-1, 1, 1)
-
-        # Create a 1D tensor of frequency values from 0 to max_freqs-1.
-        freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device)
-
-        # Reshape frequencies to be broadcastable for creating 2D basis functions.
-        # freqs_x shape: (1, max_freqs, 1)
-        # freqs_y shape: (1, 1, max_freqs)
-        freqs_x = freqs[None, :, None]
-        freqs_y = freqs[None, None, :]
-
-        # A custom weighting coefficient, not part of standard DCT.
-        # This seems to down-weight the contribution of higher-frequency interactions.
-        coeffs = (1 + freqs_x * freqs_y) ** -1
-
-        # Calculate the 1D cosine basis functions for x and y coordinates.
-        # This is the core of the DCT formulation.
-        dct_x = torch.cos(pos_x * freqs_x * torch.pi)
-        dct_y = torch.cos(pos_y * freqs_y * torch.pi)
-
-        # Combine the 1D basis functions to create 2D basis functions by element-wise
-        # multiplication, and apply the custom coefficients. Broadcasting handles the
-        # combination of all (pos_x, freqs_x) with all (pos_y, freqs_y).
-        # The result is flattened into a feature vector for each position.
-        dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2)
-
-        return dct
-
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        """
-        Forward pass for the embedder.
-
-        Args:
-            inputs (Tensor): The input tensor of shape (B, P^2, C).
-
-        Returns:
-            Tensor: The output tensor of shape (B, P^2, hidden_size_input).
-        """
-        # Get the batch size, number of pixels, and number of channels.
-        B, P2, C = inputs.shape
-
-        # Infer the patch side length from the number of pixels (P^2).
-        patch_size = int(P2 ** 0.5)
-
-        input_dtype = inputs.dtype
-        inputs = inputs.to(dtype=self.dtype)
-
-        # Fetch the pre-computed or cached positional embeddings.
-        dct = self.fetch_pos(patch_size, inputs.device, self.dtype)
-
-        # Repeat the positional embeddings for each item in the batch.
-        dct = dct.repeat(B, 1, 1)
-
-        # Concatenate the original input features with the positional embeddings
-        # along the feature dimension.
-        inputs = torch.cat((inputs, dct), dim=-1)
-
-        # Project the combined tensor to the target hidden size.
-        return self.embedder(inputs).to(dtype=input_dtype)
-
-
-class NerfGLUBlock(nn.Module):
-    """
-    A NerfBlock using a Gated Linear Unit (GLU) like MLP.
-    """
-    def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, dtype=None, device=None, operations=None):
-        super().__init__()
-        # The total number of parameters for the MLP is increased to accommodate
-        # the gate, value, and output projection matrices.
-        # We now need to generate parameters for 3 matrices.
-        total_params = 3 * hidden_size_x**2 * mlp_ratio
-        self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device)
-        self.norm = RMSNorm(hidden_size_x, dtype=dtype, device=device, operations=operations)
-        self.mlp_ratio = mlp_ratio
-
-
-    def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
-        batch_size, num_x, hidden_size_x = x.shape
-        mlp_params = self.param_generator(s)
-
-        # Split the generated parameters into three parts for the gate, value, and output projection.
-        fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1)
-
-        # Reshape the parameters into matrices for batch matrix multiplication.
-        fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
-        fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
-        fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x)
-
-        # Normalize the generated weight matrices as in the original implementation.
-        fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2)
-        fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2)
-        fc2 = torch.nn.functional.normalize(fc2, dim=-2)
-
-        res_x = x
-        x = self.norm(x)
-
-        # Apply the final output projection.
-        x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2)
-
-        return x + res_x
-
-
-class NerfFinalLayer(nn.Module):
-    def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
-        self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
-        # So we temporarily move the channel dimension to the end for the norm operation.
-        return self.linear(self.norm(x.movedim(1, -1))).movedim(-1, 1)
-
-
-class NerfFinalLayerConv(nn.Module):
-    def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
-        self.conv = operations.Conv2d(
-            in_channels=hidden_size,
-            out_channels=out_channels,
-            kernel_size=3,
-            padding=1,
-            dtype=dtype,
-            device=device,
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
-        # So we temporarily move the channel dimension to the end for the norm operation.
-        return self.conv(self.norm(x.movedim(1, -1)).movedim(-1, 1))

comfy/ldm/chroma_radiance/model.py

@@ -1,329 +0,0 @@
-# Credits:
-# Original Flux code can be found on: https://github.com/black-forest-labs/flux
-# Chroma Radiance adaption referenced from https://github.com/lodestone-rock/flow
-
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from torch import Tensor, nn
-from einops import repeat
-import comfy.ldm.common_dit
-
-from comfy.ldm.flux.layers import EmbedND
-
-from comfy.ldm.chroma.model import Chroma, ChromaParams
-from comfy.ldm.chroma.layers import (
-    DoubleStreamBlock,
-    SingleStreamBlock,
-    Approximator,
-)
-from .layers import (
-    NerfEmbedder,
-    NerfGLUBlock,
-    NerfFinalLayer,
-    NerfFinalLayerConv,
-)
-
-
-@dataclass
-class ChromaRadianceParams(ChromaParams):
-    patch_size: int
-    nerf_hidden_size: int
-    nerf_mlp_ratio: int
-    nerf_depth: int
-    nerf_max_freqs: int
-    # Setting nerf_tile_size to 0 disables tiling.
-    nerf_tile_size: int
-    # Currently one of linear (legacy) or conv.
-    nerf_final_head_type: str
-    # None means use the same dtype as the model.
-    nerf_embedder_dtype: Optional[torch.dtype]
-
-
-class ChromaRadiance(Chroma):
-    """
-    Transformer model for flow matching on sequences.
-    """
-
-    def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
-        if operations is None:
-            raise RuntimeError("Attempt to create ChromaRadiance object without setting operations")
-        nn.Module.__init__(self)
-        self.dtype = dtype
-        params = ChromaRadianceParams(**kwargs)
-        self.params = params
-        self.patch_size = params.patch_size
-        self.in_channels = params.in_channels
-        self.out_channels = params.out_channels
-        if params.hidden_size % params.num_heads != 0:
-            raise ValueError(
-                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
-            )
-        pe_dim = params.hidden_size // params.num_heads
-        if sum(params.axes_dim) != pe_dim:
-            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
-        self.hidden_size = params.hidden_size
-        self.num_heads = params.num_heads
-        self.in_dim = params.in_dim
-        self.out_dim = params.out_dim
-        self.hidden_dim = params.hidden_dim
-        self.n_layers = params.n_layers
-        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in_patch = operations.Conv2d(
-            params.in_channels,
-            params.hidden_size,
-            kernel_size=params.patch_size,
-            stride=params.patch_size,
-            bias=True,
-            dtype=dtype,
-            device=device,
-        )
-        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
-        # set as nn identity for now, will overwrite it later.
-        self.distilled_guidance_layer = Approximator(
-                    in_dim=self.in_dim,
-                    hidden_dim=self.hidden_dim,
-                    out_dim=self.out_dim,
-                    n_layers=self.n_layers,
-                    dtype=dtype, device=device, operations=operations
-                )
-
-
-        self.double_blocks = nn.ModuleList(
-            [
-                DoubleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    qkv_bias=params.qkv_bias,
-                    dtype=dtype, device=device, operations=operations
-                )
-                for _ in range(params.depth)
-            ]
-        )
-
-        self.single_blocks = nn.ModuleList(
-            [
-                SingleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    dtype=dtype, device=device, operations=operations,
-                )
-                for _ in range(params.depth_single_blocks)
-            ]
-        )
-
-        # pixel channel concat with DCT
-        self.nerf_image_embedder = NerfEmbedder(
-            in_channels=params.in_channels,
-            hidden_size_input=params.nerf_hidden_size,
-            max_freqs=params.nerf_max_freqs,
-            dtype=params.nerf_embedder_dtype or dtype,
-            device=device,
-            operations=operations,
-        )
-
-        self.nerf_blocks = nn.ModuleList([
-            NerfGLUBlock(
-                hidden_size_s=params.hidden_size,
-                hidden_size_x=params.nerf_hidden_size,
-                mlp_ratio=params.nerf_mlp_ratio,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            ) for _ in range(params.nerf_depth)
-        ])
-
-        if params.nerf_final_head_type == "linear":
-            self.nerf_final_layer = NerfFinalLayer(
-                params.nerf_hidden_size,
-                out_channels=params.in_channels,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-        elif params.nerf_final_head_type == "conv":
-            self.nerf_final_layer_conv = NerfFinalLayerConv(
-                params.nerf_hidden_size,
-                out_channels=params.in_channels,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-        else:
-            errstr = f"Unsupported nerf_final_head_type {params.nerf_final_head_type}"
-            raise ValueError(errstr)
-
-        self.skip_mmdit = []
-        self.skip_dit = []
-        self.lite = False
-
-    @property
-    def _nerf_final_layer(self) -> nn.Module:
-        if self.params.nerf_final_head_type == "linear":
-            return self.nerf_final_layer
-        if self.params.nerf_final_head_type == "conv":
-            return self.nerf_final_layer_conv
-        # Impossible to get here as we raise an error on unexpected types on initialization.
-        raise NotImplementedError
-
-    def img_in(self, img: Tensor) -> Tensor:
-        img = self.img_in_patch(img) # -> [B, Hidden, H/P, W/P]
-        # flatten into a sequence for the transformer.
-        return img.flatten(2).transpose(1, 2) # -> [B, NumPatches, Hidden]
-
-    def forward_nerf(
-        self,
-        img_orig: Tensor,
-        img_out: Tensor,
-        params: ChromaRadianceParams,
-    ) -> Tensor:
-        B, C, H, W = img_orig.shape
-        num_patches = img_out.shape[1]
-        patch_size = params.patch_size
-
-        # Store the raw pixel values of each patch for the NeRF head later.
-        # unfold creates patches: [B, C * P * P, NumPatches]
-        nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size)
-        nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P]
-
-        if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size:
-            # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than
-            # the tile size.
-            img_dct = self.forward_tiled_nerf(img_out, nerf_pixels, B, C, num_patches, patch_size, params)
-        else:
-            # Reshape for per-patch processing
-            nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
-            nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
-
-            # Get DCT-encoded pixel embeddings [pixel-dct]
-            img_dct = self.nerf_image_embedder(nerf_pixels)
-
-            # Pass through the dynamic MLP blocks (the NeRF)
-            for block in self.nerf_blocks:
-                img_dct = block(img_dct, nerf_hidden)
-
-        # Reassemble the patches into the final image.
-        img_dct = img_dct.transpose(1, 2) # -> [B*NumPatches, C, P*P]
-        # Reshape to combine with batch dimension for fold
-        img_dct = img_dct.reshape(B, num_patches, -1) # -> [B, NumPatches, C*P*P]
-        img_dct = img_dct.transpose(1, 2) # -> [B, C*P*P, NumPatches]
-        img_dct = nn.functional.fold(
-            img_dct,
-            output_size=(H, W),
-            kernel_size=patch_size,
-            stride=patch_size,
-        )
-        return self._nerf_final_layer(img_dct)
-
-    def forward_tiled_nerf(
-        self,
-        nerf_hidden: Tensor,
-        nerf_pixels: Tensor,
-        batch: int,
-        channels: int,
-        num_patches: int,
-        patch_size: int,
-        params: ChromaRadianceParams,
-    ) -> Tensor:
-        """
-        Processes the NeRF head in tiles to save memory.
-        nerf_hidden has shape [B, L, D]
-        nerf_pixels has shape [B, L, C * P * P]
-        """
-        tile_size = params.nerf_tile_size
-        output_tiles = []
-        # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1.
-        for i in range(0, num_patches, tile_size):
-            end = min(i + tile_size, num_patches)
-
-            # Slice the current tile from the input tensors
-            nerf_hidden_tile = nerf_hidden[:, i:end, :]
-            nerf_pixels_tile = nerf_pixels[:, i:end, :]
-
-            # Get the actual number of patches in this tile (can be smaller for the last tile)
-            num_patches_tile = nerf_hidden_tile.shape[1]
-
-            # Reshape the tile for per-patch processing
-            # [B, NumPatches_tile, D] -> [B * NumPatches_tile, D]
-            nerf_hidden_tile = nerf_hidden_tile.reshape(batch * num_patches_tile, params.hidden_size)
-            # [B, NumPatches_tile, C*P*P] -> [B*NumPatches_tile, C, P*P] -> [B*NumPatches_tile, P*P, C]
-            nerf_pixels_tile = nerf_pixels_tile.reshape(batch * num_patches_tile, channels, patch_size**2).transpose(1, 2)
-
-            # get DCT-encoded pixel embeddings [pixel-dct]
-            img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)
-
-            # pass through the dynamic MLP blocks (the NeRF)
-            for block in self.nerf_blocks:
-                img_dct_tile = block(img_dct_tile, nerf_hidden_tile)
-
-            output_tiles.append(img_dct_tile)
-
-        # Concatenate the processed tiles along the patch dimension
-        return torch.cat(output_tiles, dim=0)
-
-    def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams:
-        params = self.params
-        if not overrides:
-            return params
-        params_dict = {k: getattr(params, k) for k in params.__dataclass_fields__}
-        nullable_keys = frozenset(("nerf_embedder_dtype",))
-        bad_keys = tuple(k for k in overrides if k not in params_dict)
-        if bad_keys:
-            e = f"Unknown key(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
-            raise ValueError(e)
-        bad_keys = tuple(
-            k
-            for k, v in overrides.items()
-            if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys)
-        )
-        if bad_keys:
-            e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
-            raise ValueError(e)
-        # At this point it's all valid keys and values so we can merge with the existing params.
-        params_dict |= overrides
-        return params.__class__(**params_dict)
-
-    def _forward(
-        self,
-        x: Tensor,
-        timestep: Tensor,
-        context: Tensor,
-        guidance: Optional[Tensor],
-        control: Optional[dict]=None,
-        transformer_options: dict={},
-        **kwargs: dict,
-    ) -> Tensor:
-        bs, c, h, w = x.shape
-        img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
-
-        if img.ndim != 4:
-            raise ValueError("Input img tensor must be in [B, C, H, W] format.")
-        if context.ndim != 3:
-            raise ValueError("Input txt tensors must have 3 dimensions.")
-
-        params = self.radiance_get_override_params(transformer_options.get("chroma_radiance_options", {}))
-
-        h_len = (img.shape[-2] // self.patch_size)
-        w_len = (img.shape[-1] // self.patch_size)
-
-        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
-        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-
-        img_out = self.forward_orig(
-            img,
-            img_ids,
-            context,
-            txt_ids,
-            timestep,
-            guidance,
-            control,
-            transformer_options,
-            attn_mask=kwargs.get("attention_mask", None),
-        )
-        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]

comfy/ldm/cosmos/blocks.py

@@ -26,6 +26,16 @@ from torch import nn
 from comfy.ldm.modules.attention import optimized_attention
 
 
+def apply_rotary_pos_emb(
+    t: torch.Tensor,
+    freqs: torch.Tensor,
+) -> torch.Tensor:
+    t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float()
+    t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1]
+    t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t)
+    return t_out
+
+
 def get_normalization(name: str, channels: int, weight_args={}, operations=None):
     if name == "I":
         return nn.Identity()
@@ -176,7 +186,6 @@ class Attention(nn.Module):
         context=None,
         mask=None,
         rope_emb=None,
-        transformer_options={},
         **kwargs,
     ):
         """
@@ -185,7 +194,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
-        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
         del q, k, v
         out = rearrange(out, " b n s c -> s b (n c)")
         return self.to_out(out)
@@ -547,7 +556,6 @@ class VideoAttn(nn.Module):
         context: Optional[torch.Tensor] = None,
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Forward pass for video attention.
@@ -573,7 +581,6 @@ class VideoAttn(nn.Module):
             context_M_B_D,
             crossattn_mask,
             rope_emb=rope_emb_L_1_1_D,
-            transformer_options=transformer_options,
         )
         x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W)
         return x_T_H_W_B_D
@@ -668,7 +675,6 @@ class DITBuildingBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Forward pass for dynamically configured blocks with adaptive normalization.
@@ -706,7 +712,6 @@ class DITBuildingBlock(nn.Module):
                 adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
                 context=None,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             )
         elif self.block_type in ["cross_attn", "ca"]:
             x = x + gate_1_1_1_B_D * self.block(
@@ -714,7 +719,6 @@ class DITBuildingBlock(nn.Module):
                 context=crossattn_emb,
                 crossattn_mask=crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             )
         else:
             raise ValueError(f"Unknown block type: {self.block_type}")
@@ -790,7 +794,6 @@ class GeneralDITTransformerBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         for block in self.blocks:
             x = block(
@@ -800,6 +803,5 @@ class GeneralDITTransformerBlock(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
-                transformer_options=transformer_options,
             )
         return x

comfy/ldm/cosmos/cosmos_tokenizer/utils.py

@@ -58,8 +58,7 @@ def is_odd(n: int) -> bool:
 
 
 def nonlinearity(x):
-    # x * sigmoid(x)
-    return torch.nn.functional.silu(x)
+    return x * torch.sigmoid(x)
 
 
 def Normalize(in_channels, num_groups=32):

comfy/ldm/cosmos/model.py

@@ -27,8 +27,6 @@ from torchvision import transforms
 from enum import Enum
 import logging
 
-import comfy.patcher_extension
-
 from .blocks import (
     FinalLayer,
     GeneralDITTransformerBlock,
@@ -437,42 +435,6 @@ class GeneralDIT(nn.Module):
         latent_condition_sigma: Optional[torch.Tensor] = None,
         condition_video_augment_sigma: Optional[torch.Tensor] = None,
         **kwargs,
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x,
-                timesteps,
-                context,
-                attention_mask,
-                fps,
-                image_size,
-                padding_mask,
-                scalar_feature,
-                data_type,
-                latent_condition,
-                latent_condition_sigma,
-                condition_video_augment_sigma,
-                **kwargs)
-
-    def _forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        context: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        # crossattn_emb: torch.Tensor,
-        # crossattn_mask: Optional[torch.Tensor] = None,
-        fps: Optional[torch.Tensor] = None,
-        image_size: Optional[torch.Tensor] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-        scalar_feature: Optional[torch.Tensor] = None,
-        data_type: Optional[DataType] = DataType.VIDEO,
-        latent_condition: Optional[torch.Tensor] = None,
-        latent_condition_sigma: Optional[torch.Tensor] = None,
-        condition_video_augment_sigma: Optional[torch.Tensor] = None,
-        **kwargs,
     ):
         """
         Args:
@@ -520,7 +482,6 @@ class GeneralDIT(nn.Module):
                 x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
             ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"
 
-        transformer_options = kwargs.get("transformer_options", {})
         for _, block in self.blocks.items():
             assert (
                 self.blocks["block0"].x_format == block.x_format
@@ -535,7 +496,6 @@ class GeneralDIT(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
-                transformer_options=transformer_options,
             )
 
         x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")

comfy/ldm/cosmos/position_embedding.py

@@ -66,16 +66,15 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
         h_extrapolation_ratio: float = 1.0,
         w_extrapolation_ratio: float = 1.0,
         t_extrapolation_ratio: float = 1.0,
-        enable_fps_modulation: bool = True,
         device=None,
         **kwargs,  # used for compatibility with other positional embeddings; unused in this class
     ):
         del kwargs
         super().__init__()
+        self.register_buffer("seq", torch.arange(max(len_h, len_w, len_t), dtype=torch.float, device=device))
         self.base_fps = base_fps
         self.max_h = len_h
         self.max_w = len_w
-        self.enable_fps_modulation = enable_fps_modulation
 
         dim = head_dim
         dim_h = dim // 6 * 2
@@ -133,19 +132,21 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
         temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device))
 
         B, T, H, W, _ = B_T_H_W_C
-        seq = torch.arange(max(H, W, T), dtype=torch.float, device=device)
         uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max())
         assert (
             uniform_fps or B == 1 or T == 1
         ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1"
-        half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs)
-        half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs)
+        assert (
+            H <= self.max_h and W <= self.max_w
+        ), f"Input dimensions (H={H}, W={W}) exceed the maximum dimensions (max_h={self.max_h}, max_w={self.max_w})"
+        half_emb_h = torch.outer(self.seq[:H].to(device=device), h_spatial_freqs)
+        half_emb_w = torch.outer(self.seq[:W].to(device=device), w_spatial_freqs)
 
         # apply sequence scaling in temporal dimension
-        if fps is None or self.enable_fps_modulation is False:  # image case
-            half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs)
+        if fps is None:  # image case
+            half_emb_t = torch.outer(self.seq[:T].to(device=device), temporal_freqs)
         else:
-            half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs)
+            half_emb_t = torch.outer(self.seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs)
 
         half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1)
         half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1)

comfy/ldm/cosmos/predict2.py

@@ -1,886 +0,0 @@
-# original code from: https://github.com/nvidia-cosmos/cosmos-predict2
-
-import torch
-from torch import nn
-from einops import rearrange
-from einops.layers.torch import Rearrange
-import logging
-from typing import Callable, Optional, Tuple
-import math
-
-from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis
-from torchvision import transforms
-
-import comfy.patcher_extension
-from comfy.ldm.modules.attention import optimized_attention
-
-def apply_rotary_pos_emb(
-    t: torch.Tensor,
-    freqs: torch.Tensor,
-) -> torch.Tensor:
-    t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float()
-    t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1]
-    t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t)
-    return t_out
-
-
-# ---------------------- Feed Forward Network -----------------------
-class GPT2FeedForward(nn.Module):
-    def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None:
-        super().__init__()
-        self.activation = nn.GELU()
-        self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype)
-        self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype)
-
-        self._layer_id = None
-        self._dim = d_model
-        self._hidden_dim = d_ff
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.layer1(x)
-
-        x = self.activation(x)
-        x = self.layer2(x)
-        return x
-
-
-def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
-    """Computes multi-head attention using PyTorch's native implementation.
-
-    This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
-    It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product
-    attention, and rearranges the output back to the original format.
-
-    The input tensor names use the following dimension conventions:
-
-    - B: batch size
-    - S: sequence length
-    - H: number of attention heads
-    - D: head dimension
-
-    Args:
-        q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim)
-        k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim)
-        v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim)
-
-    Returns:
-        Attention output tensor with shape (batch, seq_len, n_heads * head_dim)
-    """
-    in_q_shape = q_B_S_H_D.shape
-    in_k_shape = k_B_S_H_D.shape
-    q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])
-    k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
-    v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
-    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options)
-
-
-class Attention(nn.Module):
-    """
-    A flexible attention module supporting both self-attention and cross-attention mechanisms.
-
-    This module implements a multi-head attention layer that can operate in either self-attention
-    or cross-attention mode. The mode is determined by whether a context dimension is provided.
-    The implementation uses scaled dot-product attention and supports optional bias terms and
-    dropout regularization.
-
-    Args:
-        query_dim (int): The dimensionality of the query vectors.
-        context_dim (int, optional): The dimensionality of the context (key/value) vectors.
-            If None, the module operates in self-attention mode using query_dim. Default: None
-        n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8
-        head_dim (int, optional): The dimension of each attention head. Default: 64
-        dropout (float, optional): Dropout probability applied to the output. Default: 0.0
-        qkv_format (str, optional): Format specification for QKV tensors. Default: "bshd"
-        backend (str, optional): Backend to use for the attention operation. Default: "transformer_engine"
-
-    Examples:
-        >>> # Self-attention with 512 dimensions and 8 heads
-        >>> self_attn = Attention(query_dim=512)
-        >>> x = torch.randn(32, 16, 512)  # (batch_size, seq_len, dim)
-        >>> out = self_attn(x)  # (32, 16, 512)
-
-        >>> # Cross-attention
-        >>> cross_attn = Attention(query_dim=512, context_dim=256)
-        >>> query = torch.randn(32, 16, 512)
-        >>> context = torch.randn(32, 8, 256)
-        >>> out = cross_attn(query, context)  # (32, 16, 512)
-    """
-
-    def __init__(
-        self,
-        query_dim: int,
-        context_dim: Optional[int] = None,
-        n_heads: int = 8,
-        head_dim: int = 64,
-        dropout: float = 0.0,
-        device=None,
-        dtype=None,
-        operations=None,
-    ) -> None:
-        super().__init__()
-        logging.debug(
-            f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
-            f"{n_heads} heads with a dimension of {head_dim}."
-        )
-        self.is_selfattn = context_dim is None  # self attention
-
-        context_dim = query_dim if context_dim is None else context_dim
-        inner_dim = head_dim * n_heads
-
-        self.n_heads = n_heads
-        self.head_dim = head_dim
-        self.query_dim = query_dim
-        self.context_dim = context_dim
-
-        self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
-        self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
-
-        self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
-        self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
-
-        self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
-        self.v_norm = nn.Identity()
-
-        self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype)
-        self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity()
-
-        self.attn_op = torch_attention_op
-
-        self._query_dim = query_dim
-        self._context_dim = context_dim
-        self._inner_dim = inner_dim
-
-    def compute_qkv(
-        self,
-        x: torch.Tensor,
-        context: Optional[torch.Tensor] = None,
-        rope_emb: Optional[torch.Tensor] = None,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        q = self.q_proj(x)
-        context = x if context is None else context
-        k = self.k_proj(context)
-        v = self.v_proj(context)
-        q, k, v = map(
-            lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim),
-            (q, k, v),
-        )
-
-        def apply_norm_and_rotary_pos_emb(
-            q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor]
-        ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-            q = self.q_norm(q)
-            k = self.k_norm(k)
-            v = self.v_norm(v)
-            if self.is_selfattn and rope_emb is not None:  # only apply to self-attention!
-                q = apply_rotary_pos_emb(q, rope_emb)
-                k = apply_rotary_pos_emb(k, rope_emb)
-            return q, k, v
-
-        q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb)
-
-        return q, k, v
-
-    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
-        result = self.attn_op(q, k, v, transformer_options=transformer_options)  # [B, S, H, D]
-        return self.output_dropout(self.output_proj(result))
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        context: Optional[torch.Tensor] = None,
-        rope_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
-    ) -> torch.Tensor:
-        """
-        Args:
-            x (Tensor): The query tensor of shape [B, Mq, K]
-            context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
-        """
-        q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
-        return self.compute_attention(q, k, v, transformer_options=transformer_options)
-
-
-class Timesteps(nn.Module):
-    def __init__(self, num_channels: int):
-        super().__init__()
-        self.num_channels = num_channels
-
-    def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor:
-        assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}"
-        timesteps = timesteps_B_T.flatten().float()
-        half_dim = self.num_channels // 2
-        exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device)
-        exponent = exponent / (half_dim - 0.0)
-
-        emb = torch.exp(exponent)
-        emb = timesteps[:, None].float() * emb[None, :]
-
-        sin_emb = torch.sin(emb)
-        cos_emb = torch.cos(emb)
-        emb = torch.cat([cos_emb, sin_emb], dim=-1)
-
-        return rearrange(emb, "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1])
-
-
-class TimestepEmbedding(nn.Module):
-    def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None):
-        super().__init__()
-        logging.debug(
-            f"Using AdaLN LoRA Flag:  {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility."
-        )
-        self.in_dim = in_features
-        self.out_dim = out_features
-        self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype)
-        self.activation = nn.SiLU()
-        self.use_adaln_lora = use_adaln_lora
-        if use_adaln_lora:
-            self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype)
-        else:
-            self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype)
-
-    def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        emb = self.linear_1(sample)
-        emb = self.activation(emb)
-        emb = self.linear_2(emb)
-
-        if self.use_adaln_lora:
-            adaln_lora_B_T_3D = emb
-            emb_B_T_D = sample
-        else:
-            adaln_lora_B_T_3D = None
-            emb_B_T_D = emb
-
-        return emb_B_T_D, adaln_lora_B_T_3D
-
-
-class PatchEmbed(nn.Module):
-    """
-    PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers,
-    depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions,
-    making it suitable for video and image processing tasks. It supports dividing the input into patches
-    and embedding each patch into a vector of size `out_channels`.
-
-    Parameters:
-    - spatial_patch_size (int): The size of each spatial patch.
-    - temporal_patch_size (int): The size of each temporal patch.
-    - in_channels (int): Number of input channels. Default: 3.
-    - out_channels (int): The dimension of the embedding vector for each patch. Default: 768.
-    - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True.
-    """
-
-    def __init__(
-        self,
-        spatial_patch_size: int,
-        temporal_patch_size: int,
-        in_channels: int = 3,
-        out_channels: int = 768,
-        device=None, dtype=None, operations=None
-    ):
-        super().__init__()
-        self.spatial_patch_size = spatial_patch_size
-        self.temporal_patch_size = temporal_patch_size
-
-        self.proj = nn.Sequential(
-            Rearrange(
-                "b c (t r) (h m) (w n) -> b t h w (c r m n)",
-                r=temporal_patch_size,
-                m=spatial_patch_size,
-                n=spatial_patch_size,
-            ),
-            operations.Linear(
-                in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype
-            ),
-        )
-        self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Forward pass of the PatchEmbed module.
-
-        Parameters:
-        - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where
-            B is the batch size,
-            C is the number of channels,
-            T is the temporal dimension,
-            H is the height, and
-            W is the width of the input.
-
-        Returns:
-        - torch.Tensor: The embedded patches as a tensor, with shape b t h w c.
-        """
-        assert x.dim() == 5
-        _, _, T, H, W = x.shape
-        assert (
-            H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0
-        ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}"
-        assert T % self.temporal_patch_size == 0
-        x = self.proj(x)
-        return x
-
-
-class FinalLayer(nn.Module):
-    """
-    The final layer of video DiT.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        spatial_patch_size: int,
-        temporal_patch_size: int,
-        out_channels: int,
-        use_adaln_lora: bool = False,
-        adaln_lora_dim: int = 256,
-        device=None, dtype=None, operations=None
-    ):
-        super().__init__()
-        self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = operations.Linear(
-            hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype
-        )
-        self.hidden_size = hidden_size
-        self.n_adaln_chunks = 2
-        self.use_adaln_lora = use_adaln_lora
-        self.adaln_lora_dim = adaln_lora_dim
-        if use_adaln_lora:
-            self.adaln_modulation = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype),
-                operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype),
-            )
-        else:
-            self.adaln_modulation = nn.Sequential(
-                nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype)
-            )
-
-    def forward(
-        self,
-        x_B_T_H_W_D: torch.Tensor,
-        emb_B_T_D: torch.Tensor,
-        adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
-    ):
-        if self.use_adaln_lora:
-            assert adaln_lora_B_T_3D is not None
-            shift_B_T_D, scale_B_T_D = (
-                self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size]
-            ).chunk(2, dim=-1)
-        else:
-            shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1)
-
-        shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d"), rearrange(
-            scale_B_T_D, "b t d -> b t 1 1 d"
-        )
-
-        def _fn(
-            _x_B_T_H_W_D: torch.Tensor,
-            _norm_layer: nn.Module,
-            _scale_B_T_1_1_D: torch.Tensor,
-            _shift_B_T_1_1_D: torch.Tensor,
-        ) -> torch.Tensor:
-            return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D
-
-        x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D)
-        x_B_T_H_W_O = self.linear(x_B_T_H_W_D)
-        return x_B_T_H_W_O
-
-
-class Block(nn.Module):
-    """
-    A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation.
-    Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation.
-
-    Parameters:
-        x_dim (int): Dimension of input features
-        context_dim (int): Dimension of context features for cross-attention
-        num_heads (int): Number of attention heads
-        mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0
-        use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False
-        adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256
-
-    The block applies the following sequence:
-    1. Self-attention with AdaLN modulation
-    2. Cross-attention with AdaLN modulation
-    3. MLP with AdaLN modulation
-
-    Each component uses skip connections and layer normalization.
-    """
-
-    def __init__(
-        self,
-        x_dim: int,
-        context_dim: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        use_adaln_lora: bool = False,
-        adaln_lora_dim: int = 256,
-        device=None,
-        dtype=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.x_dim = x_dim
-        self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)
-        self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations)
-
-        self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)
-        self.cross_attn = Attention(
-            x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations
-        )
-
-        self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype)
-        self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations)
-
-        self.use_adaln_lora = use_adaln_lora
-        if self.use_adaln_lora:
-            self.adaln_modulation_self_attn = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),
-                operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),
-            )
-            self.adaln_modulation_cross_attn = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),
-                operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),
-            )
-            self.adaln_modulation_mlp = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype),
-                operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype),
-            )
-        else:
-            self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))
-            self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))
-            self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype))
-
-    def forward(
-        self,
-        x_B_T_H_W_D: torch.Tensor,
-        emb_B_T_D: torch.Tensor,
-        crossattn_emb: torch.Tensor,
-        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
-        adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
-        extra_per_block_pos_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
-    ) -> torch.Tensor:
-        if extra_per_block_pos_emb is not None:
-            x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
-
-        if self.use_adaln_lora:
-            shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = (
-                self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D
-            ).chunk(3, dim=-1)
-            shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = (
-                self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D
-            ).chunk(3, dim=-1)
-            shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = (
-                self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D
-            ).chunk(3, dim=-1)
-        else:
-            shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn(
-                emb_B_T_D
-            ).chunk(3, dim=-1)
-            shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn(
-                emb_B_T_D
-            ).chunk(3, dim=-1)
-            shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1)
-
-        # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting
-        shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d")
-        scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d")
-        gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d")
-
-        shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d")
-        scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d")
-        gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d")
-
-        shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d")
-        scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d")
-        gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d")
-
-        B, T, H, W, D = x_B_T_H_W_D.shape
-
-        def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D):
-            return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D
-
-        normalized_x_B_T_H_W_D = _fn(
-            x_B_T_H_W_D,
-            self.layer_norm_self_attn,
-            scale_self_attn_B_T_1_1_D,
-            shift_self_attn_B_T_1_1_D,
-        )
-        result_B_T_H_W_D = rearrange(
-            self.self_attn(
-                # normalized_x_B_T_HW_D,
-                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
-                None,
-                rope_emb=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
-            ),
-            "b (t h w) d -> b t h w d",
-            t=T,
-            h=H,
-            w=W,
-        )
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D
-
-        def _x_fn(
-            _x_B_T_H_W_D: torch.Tensor,
-            layer_norm_cross_attn: Callable,
-            _scale_cross_attn_B_T_1_1_D: torch.Tensor,
-            _shift_cross_attn_B_T_1_1_D: torch.Tensor,
-            transformer_options: Optional[dict] = {},
-        ) -> torch.Tensor:
-            _normalized_x_B_T_H_W_D = _fn(
-                _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D
-            )
-            _result_B_T_H_W_D = rearrange(
-                self.cross_attn(
-                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
-                    crossattn_emb,
-                    rope_emb=rope_emb_L_1_1_D,
-                    transformer_options=transformer_options,
-                ),
-                "b (t h w) d -> b t h w d",
-                t=T,
-                h=H,
-                w=W,
-            )
-            return _result_B_T_H_W_D
-
-        result_B_T_H_W_D = _x_fn(
-            x_B_T_H_W_D,
-            self.layer_norm_cross_attn,
-            scale_cross_attn_B_T_1_1_D,
-            shift_cross_attn_B_T_1_1_D,
-            transformer_options=transformer_options,
-        )
-        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
-
-        normalized_x_B_T_H_W_D = _fn(
-            x_B_T_H_W_D,
-            self.layer_norm_mlp,
-            scale_mlp_B_T_1_1_D,
-            shift_mlp_B_T_1_1_D,
-        )
-        result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D)
-        x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D
-        return x_B_T_H_W_D
-
-
-class MiniTrainDIT(nn.Module):
-    """
-    A clean impl of DIT that can load and  reproduce the training results of the original DIT model in~(cosmos 1)
-    A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
-
-    Args:
-        max_img_h (int): Maximum height of the input images.
-        max_img_w (int): Maximum width of the input images.
-        max_frames (int): Maximum number of frames in the video sequence.
-        in_channels (int): Number of input channels (e.g., RGB channels for color images).
-        out_channels (int): Number of output channels.
-        patch_spatial (tuple): Spatial resolution of patches for input processing.
-        patch_temporal (int): Temporal resolution of patches for input processing.
-        concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.
-        model_channels (int): Base number of channels used throughout the model.
-        num_blocks (int): Number of transformer blocks.
-        num_heads (int): Number of heads in the multi-head attention layers.
-        mlp_ratio (float): Expansion ratio for MLP blocks.
-        crossattn_emb_channels (int): Number of embedding channels for cross-attention.
-        pos_emb_cls (str): Type of positional embeddings.
-        pos_emb_learnable (bool): Whether positional embeddings are learnable.
-        pos_emb_interpolation (str): Method for interpolating positional embeddings.
-        min_fps (int): Minimum frames per second.
-        max_fps (int): Maximum frames per second.
-        use_adaln_lora (bool): Whether to use AdaLN-LoRA.
-        adaln_lora_dim (int): Dimension for AdaLN-LoRA.
-        rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.
-        rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.
-        rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.
-        extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.
-        extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.
-        extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.
-        extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.
-    """
-
-    def __init__(
-        self,
-        max_img_h: int,
-        max_img_w: int,
-        max_frames: int,
-        in_channels: int,
-        out_channels: int,
-        patch_spatial: int,  # tuple,
-        patch_temporal: int,
-        concat_padding_mask: bool = True,
-        # attention settings
-        model_channels: int = 768,
-        num_blocks: int = 10,
-        num_heads: int = 16,
-        mlp_ratio: float = 4.0,
-        # cross attention settings
-        crossattn_emb_channels: int = 1024,
-        # positional embedding settings
-        pos_emb_cls: str = "sincos",
-        pos_emb_learnable: bool = False,
-        pos_emb_interpolation: str = "crop",
-        min_fps: int = 1,
-        max_fps: int = 30,
-        use_adaln_lora: bool = False,
-        adaln_lora_dim: int = 256,
-        rope_h_extrapolation_ratio: float = 1.0,
-        rope_w_extrapolation_ratio: float = 1.0,
-        rope_t_extrapolation_ratio: float = 1.0,
-        extra_per_block_abs_pos_emb: bool = False,
-        extra_h_extrapolation_ratio: float = 1.0,
-        extra_w_extrapolation_ratio: float = 1.0,
-        extra_t_extrapolation_ratio: float = 1.0,
-        rope_enable_fps_modulation: bool = True,
-        image_model=None,
-        device=None,
-        dtype=None,
-        operations=None,
-    ) -> None:
-        super().__init__()
-        self.dtype = dtype
-        self.max_img_h = max_img_h
-        self.max_img_w = max_img_w
-        self.max_frames = max_frames
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.patch_spatial = patch_spatial
-        self.patch_temporal = patch_temporal
-        self.num_heads = num_heads
-        self.num_blocks = num_blocks
-        self.model_channels = model_channels
-        self.concat_padding_mask = concat_padding_mask
-        # positional embedding settings
-        self.pos_emb_cls = pos_emb_cls
-        self.pos_emb_learnable = pos_emb_learnable
-        self.pos_emb_interpolation = pos_emb_interpolation
-        self.min_fps = min_fps
-        self.max_fps = max_fps
-        self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio
-        self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio
-        self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio
-        self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb
-        self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio
-        self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio
-        self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio
-        self.rope_enable_fps_modulation = rope_enable_fps_modulation
-
-        self.build_pos_embed(device=device, dtype=dtype)
-        self.use_adaln_lora = use_adaln_lora
-        self.adaln_lora_dim = adaln_lora_dim
-        self.t_embedder = nn.Sequential(
-            Timesteps(model_channels),
-            TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,),
-        )
-
-        in_channels = in_channels + 1 if concat_padding_mask else in_channels
-        self.x_embedder = PatchEmbed(
-            spatial_patch_size=patch_spatial,
-            temporal_patch_size=patch_temporal,
-            in_channels=in_channels,
-            out_channels=model_channels,
-            device=device, dtype=dtype, operations=operations,
-        )
-
-        self.blocks = nn.ModuleList(
-            [
-                Block(
-                    x_dim=model_channels,
-                    context_dim=crossattn_emb_channels,
-                    num_heads=num_heads,
-                    mlp_ratio=mlp_ratio,
-                    use_adaln_lora=use_adaln_lora,
-                    adaln_lora_dim=adaln_lora_dim,
-                    device=device, dtype=dtype, operations=operations,
-                )
-                for _ in range(num_blocks)
-            ]
-        )
-
-        self.final_layer = FinalLayer(
-            hidden_size=self.model_channels,
-            spatial_patch_size=self.patch_spatial,
-            temporal_patch_size=self.patch_temporal,
-            out_channels=self.out_channels,
-            use_adaln_lora=self.use_adaln_lora,
-            adaln_lora_dim=self.adaln_lora_dim,
-            device=device, dtype=dtype, operations=operations,
-        )
-
-        self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype)
-
-    def build_pos_embed(self, device=None, dtype=None) -> None:
-        if self.pos_emb_cls == "rope3d":
-            cls_type = VideoRopePosition3DEmb
-        else:
-            raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}")
-
-        logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}")
-        kwargs = dict(
-            model_channels=self.model_channels,
-            len_h=self.max_img_h // self.patch_spatial,
-            len_w=self.max_img_w // self.patch_spatial,
-            len_t=self.max_frames // self.patch_temporal,
-            max_fps=self.max_fps,
-            min_fps=self.min_fps,
-            is_learnable=self.pos_emb_learnable,
-            interpolation=self.pos_emb_interpolation,
-            head_dim=self.model_channels // self.num_heads,
-            h_extrapolation_ratio=self.rope_h_extrapolation_ratio,
-            w_extrapolation_ratio=self.rope_w_extrapolation_ratio,
-            t_extrapolation_ratio=self.rope_t_extrapolation_ratio,
-            enable_fps_modulation=self.rope_enable_fps_modulation,
-            device=device,
-        )
-        self.pos_embedder = cls_type(
-            **kwargs,  # type: ignore
-        )
-
-        if self.extra_per_block_abs_pos_emb:
-            kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio
-            kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio
-            kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio
-            kwargs["device"] = device
-            kwargs["dtype"] = dtype
-            self.extra_pos_embedder = LearnablePosEmbAxis(
-                **kwargs,  # type: ignore
-            )
-
-    def prepare_embedded_sequence(
-        self,
-        x_B_C_T_H_W: torch.Tensor,
-        fps: Optional[torch.Tensor] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
-        """
-        Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.
-
-        Args:
-            x_B_C_T_H_W (torch.Tensor): video
-            fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.
-                                    If None, a default value (`self.base_fps`) will be used.
-            padding_mask (Optional[torch.Tensor]): current it is not used
-
-        Returns:
-            Tuple[torch.Tensor, Optional[torch.Tensor]]:
-                - A tensor of shape (B, T, H, W, D) with the embedded sequence.
-                - An optional positional embedding tensor, returned only if the positional embedding class
-                (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.
-
-        Notes:
-            - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.
-            - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.
-            - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using
-                the `self.pos_embedder` with the shape [T, H, W].
-            - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the
-            `self.pos_embedder` with the fps tensor.
-            - Otherwise, the positional embeddings are generated without considering fps.
-        """
-        if self.concat_padding_mask:
-            if padding_mask is None:
-                padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)
-            else:
-                padding_mask = transforms.functional.resize(
-                    padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST
-                )
-            x_B_C_T_H_W = torch.cat(
-                [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1
-            )
-        x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
-
-        if self.extra_per_block_abs_pos_emb:
-            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)
-        else:
-            extra_pos_emb = None
-
-        if "rope" in self.pos_emb_cls.lower():
-            return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb
-        x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
-
-        return x_B_T_H_W_D, None, extra_pos_emb
-
-    def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor:
-        x_B_C_Tt_Hp_Wp = rearrange(
-            x_B_T_H_W_M,
-            "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)",
-            p1=self.patch_spatial,
-            p2=self.patch_spatial,
-            t=self.patch_temporal,
-        )
-        return x_B_C_Tt_Hp_Wp
-
-    def forward(self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        context: torch.Tensor,
-        fps: Optional[torch.Tensor] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-        **kwargs,
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x, timesteps, context, fps, padding_mask, **kwargs)
-
-    def _forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        context: torch.Tensor,
-        fps: Optional[torch.Tensor] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-        **kwargs,
-    ):
-        x_B_C_T_H_W = x
-        timesteps_B_T = timesteps
-        crossattn_emb = context
-        """
-        Args:
-            x: (B, C, T, H, W) tensor of spatial-temp inputs
-            timesteps: (B, ) tensor of timesteps
-            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
-        """
-        x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(
-            x_B_C_T_H_W,
-            fps=fps,
-            padding_mask=padding_mask,
-        )
-
-        if timesteps_B_T.ndim == 1:
-            timesteps_B_T = timesteps_B_T.unsqueeze(1)
-        t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype))
-        t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D)
-
-        # for logging purpose
-        affline_scale_log_info = {}
-        affline_scale_log_info["t_embedding_B_T_D"] = t_embedding_B_T_D.detach()
-        self.affline_scale_log_info = affline_scale_log_info
-        self.affline_emb = t_embedding_B_T_D
-        self.crossattn_emb = crossattn_emb
-
-        if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
-            assert (
-                x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
-            ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}"
-
-        block_kwargs = {
-            "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
-            "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
-            "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
-            "transformer_options": kwargs.get("transformer_options", {}),
-        }
-        for block in self.blocks:
-            x_B_T_H_W_D = block(
-                x_B_T_H_W_D,
-                t_embedding_B_T_D,
-                crossattn_emb,
-                **block_kwargs,
-            )
-
-        x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D)
-        x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O)
-        return x_B_C_Tt_Hp_Wp

comfy/ldm/flux/controlnet.py

@@ -121,11 +121,6 @@ class ControlNetFlux(Flux):
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
 
-        if y is None:
-            y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
-        else:
-            y = y[:, :self.params.vec_in_dim]
-
         # running on sequences img
         img = self.img_in(img)
 
@@ -179,7 +174,7 @@ class ControlNetFlux(Flux):
             out["output"] = out_output[:self.main_model_single]
         return out
 
-    def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs):
+    def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
         patch_size = 2
         if self.latent_input:
             hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))

comfy/ldm/flux/layers.py

@@ -118,7 +118,7 @@ class Modulation(nn.Module):
 def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
     if modulation_dims is None:
         if m_add is not None:
-            return torch.addcmul(m_add, tensor, m_mult)
+            return tensor * m_mult + m_add
         else:
             return tensor * m_mult
     else:
@@ -159,7 +159,7 @@ class DoubleStreamBlock(nn.Module):
         )
         self.flipped_img_txt = flipped_img_txt
 
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None):
         img_mod1, img_mod2 = self.img_mod(vec)
         txt_mod1, txt_mod2 = self.txt_mod(vec)
 
@@ -182,7 +182,7 @@ class DoubleStreamBlock(nn.Module):
             attn = attention(torch.cat((img_q, txt_q), dim=2),
                              torch.cat((img_k, txt_k), dim=2),
                              torch.cat((img_v, txt_v), dim=2),
-                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+                             pe=pe, mask=attn_mask)
 
             img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:]
         else:
@@ -190,7 +190,7 @@ class DoubleStreamBlock(nn.Module):
             attn = attention(torch.cat((txt_q, img_q), dim=2),
                              torch.cat((txt_k, img_k), dim=2),
                              torch.cat((txt_v, img_v), dim=2),
-                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+                             pe=pe, mask=attn_mask)
 
             txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
@@ -244,7 +244,7 @@ class SingleStreamBlock(nn.Module):
         self.mlp_act = nn.GELU(approximate="tanh")
         self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
 
-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor:
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor:
         mod, _ = self.modulation(vec)
         qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
@@ -252,7 +252,7 @@ class SingleStreamBlock(nn.Module):
         q, k = self.norm(q, k, v)
 
         # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
         x += apply_mod(output, mod.gate, None, modulation_dims)

comfy/ldm/flux/math.py

@@ -6,7 +6,7 @@ from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
     q_shape = q.shape
     k_shape = k.shape
 
@@ -17,7 +17,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transforme
         k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
 
     heads = q.shape[1]
-    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
+    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
     return x
 
 
@@ -35,13 +35,11 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
     return out.to(dtype=torch.float32, device=pos.device)
 
-def apply_rope1(x: Tensor, freqs_cis: Tensor):
-    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
-
-    x_out = freqs_cis[..., 0] * x_[..., 0]
-    x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
-
-    return x_out.reshape(*x.shape).type_as(x)
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
+    xq_ = xq.to(dtype=freqs_cis.dtype).reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.to(dtype=freqs_cis.dtype).reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+

comfy/ldm/flux/model.py

@@ -6,7 +6,6 @@ import torch
 from torch import Tensor, nn
 from einops import rearrange, repeat
 import comfy.ldm.common_dit
-import comfy.patcher_extension
 
 from .layers import (
     DoubleStreamBlock,
@@ -102,11 +101,6 @@ class Flux(nn.Module):
         transformer_options={},
         attn_mask: Tensor = None,
     ) -> Tensor:
-
-        if y is None:
-            y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
-
-        patches = transformer_options.get("patches", {})
         patches_replace = transformer_options.get("patches_replace", {})
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -118,17 +112,9 @@ class Flux(nn.Module):
             if guidance is not None:
                 vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
-        if "post_input" in patches:
-            for p in patches["post_input"]:
-                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
-                img = out["img"]
-                txt = out["txt"]
-                img_ids = out["img_ids"]
-                txt_ids = out["txt_ids"]
-
         if img_ids is not None:
             ids = torch.cat((txt_ids, img_ids), dim=1)
             pe = self.pe_embedder(ids)
@@ -144,16 +130,14 @@ class Flux(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args.get("transformer_options"))
+                                                   attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -162,18 +146,14 @@ class Flux(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 attn_mask=attn_mask)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
                 if i < len(control_i):
                     add = control_i[i]
                     if add is not None:
-                        img[:, :add.shape[1]] += add
-
-        if img.dtype == torch.float16:
-            img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504)
+                        img += add
 
         img = torch.cat((txt, img), 1)
 
@@ -184,97 +164,44 @@ class Flux(nn.Module):
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"),
-                                       transformer_options=args.get("transformer_options"))
+                                       attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
                 if i < len(control_o):
                     add = control_o[i]
                     if add is not None:
-                        img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add
+                        img[:, txt.shape[1] :, ...] += add
 
         img = img[:, txt.shape[1] :, ...]
 
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def process_img(self, x, index=0, h_offset=0, w_offset=0):
+    def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
 
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
-
-        h_offset = ((h_offset + (patch_size // 2)) // patch_size)
-        w_offset = ((w_offset + (patch_size // 2)) // patch_size)
-
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 0] = img_ids[:, :, 1] + index
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
-        return img, repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs):
-        bs, c, h_orig, w_orig = x.shape
-        patch_size = self.patch_size
-
-        h_len = ((h_orig + (patch_size // 2)) // patch_size)
-        w_len = ((w_orig + (patch_size // 2)) // patch_size)
-        img, img_ids = self.process_img(x)
-        img_tokens = img.shape[1]
-        if ref_latents is not None:
-            h = 0
-            w = 0
-            index = 0
-            ref_latents_method = kwargs.get("ref_latents_method", "offset")
-            for ref in ref_latents:
-                if ref_latents_method == "index":
-                    index += 1
-                    h_offset = 0
-                    w_offset = 0
-                elif ref_latents_method == "uxo":
-                    index = 0
-                    h_offset = h_len * patch_size + h
-                    w_offset = w_len * patch_size + w
-                    h += ref.shape[-2]
-                    w += ref.shape[-1]
-                else:
-                    index = 1
-                    h_offset = 0
-                    w_offset = 0
-                    if ref.shape[-2] + h > ref.shape[-1] + w:
-                        w_offset = w
-                    else:
-                        h_offset = h
-                    h = max(h, ref.shape[-2] + h_offset)
-                    w = max(w, ref.shape[-1] + w_offset)
-
-                kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
-                img = torch.cat([img, kontext], dim=1)
-                img_ids = torch.cat([img_ids, kontext_ids], dim=1)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
-        out = out[:, :img_tokens]
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig]
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -109,7 +109,6 @@ class AsymmetricAttention(nn.Module):
         scale_x: torch.Tensor,  # (B, dim_x), modulation for pre-RMSNorm.
         scale_y: torch.Tensor,  # (B, dim_y), modulation for pre-RMSNorm.
         crop_y,
-        transformer_options={},
         **rope_rotation,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         rope_cos = rope_rotation.get("rope_cos")
@@ -144,7 +143,7 @@ class AsymmetricAttention(nn.Module):
 
         xy = optimized_attention(q,
                                  k,
-                                 v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)
+                                 v, self.num_heads, skip_reshape=True)
 
         x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
         x = self.proj_x(x)
@@ -225,7 +224,6 @@ class AsymmetricJointBlock(nn.Module):
         x: torch.Tensor,
         c: torch.Tensor,
         y: torch.Tensor,
-        transformer_options={},
         **attn_kwargs,
     ):
         """Forward pass of a block.
@@ -258,7 +256,6 @@ class AsymmetricJointBlock(nn.Module):
             y,
             scale_x=scale_msa_x,
             scale_y=scale_msa_y,
-            transformer_options=transformer_options,
             **attn_kwargs,
         )
 
@@ -527,11 +524,10 @@ class AsymmDiTJoint(nn.Module):
                                                     args["txt"],
                                                     rope_cos=args["rope_cos"],
                                                     rope_sin=args["rope_sin"],
-                                                    crop_y=args["num_tokens"],
-                                                    transformer_options=args["transformer_options"]
+                                                    crop_y=args["num_tokens"]
                                                     )
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
                 y_feat = out["txt"]
                 x = out["img"]
             else:
@@ -542,7 +538,6 @@ class AsymmDiTJoint(nn.Module):
                     rope_cos=rope_cos,
                     rope_sin=rope_sin,
                     crop_y=num_tokens,
-                    transformer_options=transformer_options,
                 )  # (B, M, D), (B, L, D)
         del y_feat  # Final layers don't use dense text features.
 

comfy/ldm/hidream/model.py

@@ -13,7 +13,6 @@ from comfy.ldm.flux.layers import LastLayer
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
-import comfy.patcher_extension
 import comfy.ldm.common_dit
 
 
@@ -72,8 +71,8 @@ class TimestepEmbed(nn.Module):
         return t_emb
 
 
-def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}):
-    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options)
+def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor):
+    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2])
 
 
 class HiDreamAttnProcessor_flashattn:
@@ -86,7 +85,6 @@ class HiDreamAttnProcessor_flashattn:
         image_tokens_masks: Optional[torch.FloatTensor] = None,
         text_tokens: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
         *args,
         **kwargs,
     ) -> torch.FloatTensor:
@@ -134,7 +132,7 @@ class HiDreamAttnProcessor_flashattn:
             query = torch.cat([query_1, query_2], dim=-1)
             key = torch.cat([key_1, key_2], dim=-1)
 
-        hidden_states = attention(query, key, value, transformer_options=transformer_options)
+        hidden_states = attention(query, key, value)
 
         if not attn.single:
             hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1)
@@ -200,7 +198,6 @@ class HiDreamAttention(nn.Module):
         image_tokens_masks: torch.FloatTensor = None,
         norm_text_tokens: torch.FloatTensor = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.Tensor:
         return self.processor(
             self,
@@ -208,7 +205,6 @@ class HiDreamAttention(nn.Module):
             image_tokens_masks = image_tokens_masks,
             text_tokens = norm_text_tokens,
             rope = rope,
-            transformer_options=transformer_options,
         )
 
 
@@ -409,7 +405,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
+
     ) -> torch.FloatTensor:
         wtype = image_tokens.dtype
         shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \
@@ -422,7 +418,6 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
             norm_image_tokens,
             image_tokens_masks,
             rope = rope,
-            transformer_options=transformer_options,
         )
         image_tokens = gate_msa_i * attn_output_i + image_tokens
 
@@ -487,7 +482,6 @@ class HiDreamImageTransformerBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.FloatTensor:
         wtype = image_tokens.dtype
         shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \
@@ -505,7 +499,6 @@ class HiDreamImageTransformerBlock(nn.Module):
             image_tokens_masks,
             norm_text_tokens,
             rope = rope,
-            transformer_options=transformer_options,
         )
 
         image_tokens = gate_msa_i * attn_output_i + image_tokens
@@ -556,7 +549,6 @@ class HiDreamImageBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: torch.FloatTensor = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.FloatTensor:
         return self.block(
             image_tokens,
@@ -564,7 +556,6 @@ class HiDreamImageBlock(nn.Module):
             text_tokens,
             adaln_input,
             rope,
-            transformer_options=transformer_options,
         )
 
 
@@ -701,23 +692,7 @@ class HiDreamImageTransformer2DModel(nn.Module):
             raise NotImplementedError
         return x, x_masks, img_sizes
 
-    def forward(self,
-        x: torch.Tensor,
-        t: torch.Tensor,
-        y: Optional[torch.Tensor] = None,
-        context: Optional[torch.Tensor] = None,
-        encoder_hidden_states_llama3=None,
-        image_cond=None,
-        control = None,
-        transformer_options = {},
-    ):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, t, y, context, encoder_hidden_states_llama3, image_cond, control, transformer_options)
-
-    def _forward(
+    def forward(
         self,
         x: torch.Tensor,
         t: torch.Tensor,
@@ -794,7 +769,6 @@ class HiDreamImageTransformer2DModel(nn.Module):
                 text_tokens = cur_encoder_hidden_states,
                 adaln_input = adaln_input,
                 rope = rope,
-                transformer_options=transformer_options,
             )
             initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len]
             block_id += 1
@@ -818,7 +792,6 @@ class HiDreamImageTransformer2DModel(nn.Module):
                 text_tokens=None,
                 adaln_input=adaln_input,
                 rope=rope,
-                transformer_options=transformer_options,
             )
             hidden_states = hidden_states[:, :hidden_states_seq_len]
             block_id += 1

comfy/ldm/hunyuan3d/model.py

@@ -7,7 +7,6 @@ from comfy.ldm.flux.layers import (
     SingleStreamBlock,
     timestep_embedding,
 )
-import comfy.patcher_extension
 
 
 class Hunyuan3Dv2(nn.Module):
@@ -68,13 +67,6 @@ class Hunyuan3Dv2(nn.Module):
         self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations)
 
     def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, guidance, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs):
         x = x.movedim(-1, -2)
         timestep = 1.0 - timestep
         txt = context
@@ -99,16 +91,14 @@ class Hunyuan3Dv2(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args["transformer_options"])
+                                                   attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -117,8 +107,7 @@ class Hunyuan3Dv2(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 attn_mask=attn_mask)
 
         img = torch.cat((txt, img), 1)
 
@@ -129,19 +118,17 @@ class Hunyuan3Dv2(nn.Module):
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"),
-                                       transformer_options=args["transformer_options"])
+                                       attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
 
         img = img[:, txt.shape[1]:, ...]
         img = self.final_layer(img, vec)

comfy/ldm/hunyuan3d/vae.py

@@ -4,458 +4,81 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+
+
+from typing import Union, Tuple, List, Callable, Optional
+
 import numpy as np
-import math
+from einops import repeat, rearrange
 from tqdm import tqdm
-
-from typing import Optional
-
 import logging
 
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True):
-
-    # manually create the pointer vector
-    assert src.size(0) == batch.numel()
-
-    batch_size = int(batch.max()) + 1
-    deg = src.new_zeros(batch_size, dtype = torch.long)
-
-    deg.scatter_add_(0, batch, torch.ones_like(batch))
-
-    ptr_vec = deg.new_zeros(batch_size + 1)
-    torch.cumsum(deg, 0, out=ptr_vec[1:])
-
-    #return fps_sampling(src, ptr_vec, ratio)
-    sampled_indicies = []
-
-    for b in range(batch_size):
-        # start and the end of each batch
-        start, end = ptr_vec[b].item(), ptr_vec[b + 1].item()
-        # points from the point cloud
-        points = src[start:end]
-
-        num_points = points.size(0)
-        num_samples = max(1, math.ceil(num_points * sampling_ratio))
-
-        selected = torch.zeros(num_samples, device = src.device, dtype = torch.long)
-        distances = torch.full((num_points,), float("inf"), device = src.device)
-
-        # select a random start point
-        if start_random:
-            farthest = torch.randint(0, num_points, (1,), device = src.device)
-        else:
-            farthest = torch.tensor([0], device = src.device, dtype = torch.long)
-
-        for i in range(num_samples):
-            selected[i] = farthest
-            centroid = points[farthest].squeeze(0)
-            dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance
-            distances = torch.minimum(distances, dist)
-            farthest = torch.argmax(distances)
-
-        sampled_indicies.append(torch.arange(start, end)[selected])
-
-    return torch.cat(sampled_indicies, dim = 0)
-class PointCrossAttention(nn.Module):
-    def __init__(self,
-        num_latents: int,
-        downsample_ratio: float,
-        pc_size: int,
-        pc_sharpedge_size: int,
-        point_feats: int,
-        width: int,
-        heads: int,
-        layers: int,
-        fourier_embedder,
-        normal_pe: bool = False,
-        qkv_bias: bool = False,
-        use_ln_post: bool = True,
-        qk_norm: bool = True):
-
-        super().__init__()
-
-        self.fourier_embedder = fourier_embedder
-
-        self.pc_size = pc_size
-        self.normal_pe = normal_pe
-        self.downsample_ratio = downsample_ratio
-        self.pc_sharpedge_size = pc_sharpedge_size
-        self.num_latents = num_latents
-        self.point_feats = point_feats
-
-        self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width)
-
-        self.cross_attn = ResidualCrossAttentionBlock(
-            width = width,
-            heads = heads,
-            qkv_bias = qkv_bias,
-            qk_norm = qk_norm
-        )
-
-        self.self_attn = None
-        if layers > 0:
-            self.self_attn = Transformer(
-                width = width,
-                heads = heads,
-                qkv_bias = qkv_bias,
-                qk_norm = qk_norm,
-                layers = layers
-            )
-
-        if use_ln_post:
-            self.ln_post = nn.LayerNorm(width)
-        else:
-            self.ln_post = None
-
-    def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor):
-
-        """
-        Subsample points randomly from the point cloud (input_pc)
-        Further sample the subsampled points to get query_pc
-        take the fourier embeddings for both input and query pc
-
-        Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc.
-        Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc).
-        More computationally efficient.
-
-        Features are additional information for each point in the cloud
-        """
-
-        B, _, D = point_cloud.shape
-
-        num_latents = int(self.num_latents)
-
-        num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents
-        num_sharpedge_query = num_latents - num_random_query
-
-        # Split random and sharpedge surface points
-        random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1)
-
-        # assert statements
-        assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size"
-        assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size"
-
-        input_random_pc_size = int(num_random_query * self.downsample_ratio)
-        random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \
-            self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size)
-
-        input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio)
-
-        if input_sharpedge_pc_size == 0:
-            sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device)
-            sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device)
-
-        else:
-            sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \
-            self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size)
-
-        # concat the random and sharpedges
-        query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1)
-        input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1)
-
-        query = self.fourier_embedder(query_pc)
-        data = self.fourier_embedder(input_pc)
-
-        if self.point_feats > 0:
-            random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1)
-
-            input_random_surface_features, query_random_features = \
-                self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B,
-                                     input_pc_size = input_random_pc_size, idx_query = random_idx_query)
-
-            if input_sharpedge_pc_size == 0:
-                input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats,
-                                                               dtype = input_random_surface_features.dtype, device = point_cloud.device)
-
-                query_sharpedge_features = torch.zeros(B, 0, self.point_feats,
-                                                       dtype = query_random_features.dtype, device = point_cloud.device)
-            else:
-
-                input_sharpedge_surface_features, query_sharpedge_features = \
-                    self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features,
-                                         batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size)
-
-            query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1)
-            input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1)
-
-            if self.normal_pe:
-                # apply the fourier embeddings on the first 3 dims (xyz)
-                input_features_pe = self.fourier_embedder(input_features[..., :3])
-                query_features_pe = self.fourier_embedder(query_features[..., :3])
-                # replace the first 3 dims with the new PE ones
-                input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1)
-                query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1)
-
-            # concat at the channels dim
-            query = torch.cat([query, query_features], dim = -1)
-            data = torch.cat([data, input_features], dim = -1)
-
-        # don't return pc_info to avoid unnecessary memory usuage
-        return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1])
-
-    def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):
-
-        query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features)
-
-        # apply projections
-        query = self.input_proj(query)
-        data = self.input_proj(data)
-
-        # apply cross attention between query and data
-        latents = self.cross_attn(query, data)
-
-        if self.self_attn is not None:
-            latents = self.self_attn(latents)
-
-        if self.ln_post is not None:
-            latents = self.ln_post(latents)
-
-        return latents
-
-
-    def subsample(self, pc, num_query, input_pc_size: int):
-
-        """
-        num_query: number of points to keep after FPS
-        input_pc_size: number of points to select before FPS
-        """
-
-        B, _, D = pc.shape
-        query_ratio = num_query / input_pc_size
-
-        # random subsampling of points inside the point cloud
-        idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size]
-        input_pc = pc[:, idx_pc, :]
-
-        # flatten to allow applying fps across the whole batch
-        flattent_input_pc = input_pc.view(B * input_pc_size, D)
-
-        # construct a batch_down tensor to tell fps
-        # which points belong to which batch
-        N_down = int(flattent_input_pc.shape[0] / B)
-        batch_down = torch.arange(B).to(pc.device)
-        batch_down = torch.repeat_interleave(batch_down, N_down)
-
-        idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio)
-        query_pc = flattent_input_pc[idx_query].view(B, -1, D)
-
-        return query_pc, input_pc, idx_pc, idx_query
-
-    def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query):
-
-        B = batch_size
-
-        input_surface_features = features[:, idx_pc, :]
-        flattent_input_features = input_surface_features.view(B * input_pc_size, -1)
-        query_features = flattent_input_features[idx_query].view(B, -1,
-                                                                 flattent_input_features.shape[-1])
-
-        return input_surface_features, query_features
-
-def normalize_mesh(mesh, scale = 0.9999):
-    """Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]"""
-
-    bbox = mesh.bounds
-    center = (bbox[1] + bbox[0]) / 2
-
-    max_extent = (bbox[1] - bbox[0]).max()
-    mesh.apply_translation(-center)
-    mesh.apply_scale((2 * scale) / max_extent)
-
-    return mesh
-
-def sample_pointcloud(mesh, num = 200000):
-    """ Uniformly sample points from the surface of the mesh """
-
-    points, face_idx = mesh.sample(num, return_index = True)
-    normals = mesh.face_normals[face_idx]
-    return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32))
-
-def detect_sharp_edges(mesh, threshold=0.985):
-    """Return edge indices (a, b) that lie on sharp boundaries of the mesh."""
-
-    V, F = mesh.vertices, mesh.faces
-    VN, FN = mesh.vertex_normals, mesh.face_normals
-
-    sharp_mask = np.ones(V.shape[0])
-    for i in range(3):
-        indices = F[:, i]
-        alignment = np.einsum('ij,ij->i', VN[indices], FN)
-        dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1)
-        sharp_mask[indices] = np.min(dot_stack, axis=-1)
-
-    edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]])
-    edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]])
-    sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold)
-
-    return edge_a[sharp_edges], edge_b[sharp_edges]
-
-
-def sharp_sample_pointcloud(mesh, num = 16384):
-    """ Sample points preferentially from sharp edges in the mesh. """
-
-    edge_a, edge_b = detect_sharp_edges(mesh)
-    V, VN = mesh.vertices, mesh.vertex_normals
-
-    va, vb = V[edge_a], V[edge_b]
-    na, nb = VN[edge_a], VN[edge_b]
-
-    edge_lengths = np.linalg.norm(vb - va, axis=-1)
-    weights = edge_lengths / edge_lengths.sum()
-
-    indices = np.searchsorted(np.cumsum(weights), np.random.rand(num))
-    t = np.random.rand(num, 1)
-
-    samples = t * va[indices] + (1 - t) * vb[indices]
-    normals = t * na[indices] + (1 - t) * nb[indices]
-
-    return samples.astype(np.float32), normals.astype(np.float32)
-
-def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"):
-    """Load a surface with optional sharp-edge annotations from a trimesh mesh."""
-
-    import trimesh
-
-    try:
-        mesh_full = trimesh.util.concatenate(mesh.dump())
-    except Exception:
-        mesh_full = trimesh.util.concatenate(mesh)
-
-    mesh_full = normalize_mesh(mesh_full)
-
-    faces = mesh_full.faces
-    vertices = mesh_full.vertices
-    origin_face_count = faces.shape[0]
-
-    mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count])
-    mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:])
-
-    area_surface = mesh_surface.area
-    area_fill = mesh_fill.area
-    total_area = area_surface + area_fill
-
-    sample_num = 499712 // 2
-    fill_ratio = area_fill / total_area if total_area > 0 else 0
-
-    num_fill = int(sample_num * fill_ratio)
-    num_surface = sample_num - num_fill
-
-    surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface)
-    fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill)
-
-    sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num)
-
-    def assemble_tensor(points, normals, label=None):
-
-        data = torch.cat([points, normals], dim=1).half().to(device)
-
-        if label is not None:
-            label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device)
-            data = torch.cat([data, label_tensor], dim=1)
-
-        return data
-
-    surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0),
-                              torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0),
-                              label = 0 if sharpedge_flag else None)
-
-    sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals),
-                                    label = 1 if sharpedge_flag else None)
-
-    rng = np.random.default_rng()
-
-    surface = surface[rng.choice(surface.shape[0], num_points, replace = False)]
-    sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)]
-
-    full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0)
-
-    return full
-
-class SharpEdgeSurfaceLoader:
-    """ Load mesh surface and sharp edge samples. """
-
-    def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):
-
-        self.num_uniform_points = num_uniform_points
-        self.num_sharp_points = num_sharp_points
-        self.total_points = num_uniform_points + num_sharp_points
-
-    def __call__(self, mesh_input, device = "cuda"):
-        mesh = self._load_mesh(mesh_input)
-        return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device)
-
-    @staticmethod
-    def _load_mesh(mesh_input):
-        import trimesh
-
-        if isinstance(mesh_input, str):
-            mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True)
-        else:
-            mesh = mesh_input
-
-        if isinstance(mesh, trimesh.Scene):
-            combined = None
-            for obj in mesh.geometry.values():
-                combined = obj if combined is None else combined + obj
-            return combined
-
-        return mesh
-
-class DiagonalGaussianDistribution:
-    def __init__(self, params: torch.Tensor, feature_dim: int = -1):
-
-        # divide quant channels (8) into mean and log variance
-        self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim)
-
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.std = torch.exp(0.5 * self.logvar)
-
-    def sample(self):
-
-        eps = torch.randn_like(self.std)
-        z = self.mean + eps * self.std
-
-        return z
-
-################################################
-# Volume Decoder
-################################################
-
-class VanillaVolumeDecoder():
+def generate_dense_grid_points(
+    bbox_min: np.ndarray,
+    bbox_max: np.ndarray,
+    octree_resolution: int,
+    indexing: str = "ij",
+):
+    length = bbox_max - bbox_min
+    num_cells = octree_resolution
+
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1)
+    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
+
+    return xyz, grid_size, length
+
+
+class VanillaVolumeDecoder:
     @torch.no_grad()
-    def __call__(self, latents: torch.Tensor, geo_decoder: callable, octree_resolution: int, bounds = 1.01,
-                 num_chunks: int = 10_000, enable_pbar: bool = True, **kwargs):
+    def __call__(
+        self,
+        latents: torch.FloatTensor,
+        geo_decoder: Callable,
+        bounds: Union[Tuple[float], List[float], float] = 1.01,
+        num_chunks: int = 10000,
+        octree_resolution: int = None,
+        enable_pbar: bool = True,
+        **kwargs,
+    ):
+        device = latents.device
+        dtype = latents.dtype
+        batch_size = latents.shape[0]
 
+        # 1. generate query points
         if isinstance(bounds, float):
             bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
 
-        bbox_min, bbox_max = torch.tensor(bounds[:3]), torch.tensor(bounds[3:])
-
-        x = torch.linspace(bbox_min[0], bbox_max[0], int(octree_resolution) + 1, dtype = torch.float32)
-        y = torch.linspace(bbox_min[1], bbox_max[1], int(octree_resolution) + 1, dtype = torch.float32)
-        z = torch.linspace(bbox_min[2], bbox_max[2], int(octree_resolution) + 1, dtype = torch.float32)
-
-        [xs, ys, zs] = torch.meshgrid(x, y, z, indexing = "ij")
-        xyz = torch.stack((xs, ys, zs), axis=-1).to(latents.device, dtype = latents.dtype).contiguous().reshape(-1, 3)
-        grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1]
+        bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6])
+        xyz_samples, grid_size, length = generate_dense_grid_points(
+            bbox_min=bbox_min,
+            bbox_max=bbox_max,
+            octree_resolution=octree_resolution,
+            indexing="ij"
+        )
+        xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3)
 
+        # 2. latents to 3d volume
         batch_logits = []
-        for start in tqdm(range(0, xyz.shape[0], num_chunks), desc="Volume Decoding",
+        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc="Volume Decoding",
                           disable=not enable_pbar):
-
-            chunk_queries = xyz[start: start + num_chunks, :]
-            chunk_queries = chunk_queries.unsqueeze(0).repeat(latents.shape[0], 1, 1)
-            logits = geo_decoder(queries = chunk_queries, latents = latents)
+            chunk_queries = xyz_samples[start: start + num_chunks, :]
+            chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size)
+            logits = geo_decoder(queries=chunk_queries, latents=latents)
             batch_logits.append(logits)
 
-        grid_logits = torch.cat(batch_logits, dim = 1)
-        grid_logits = grid_logits.view((latents.shape[0], *grid_size)).float()
+        grid_logits = torch.cat(batch_logits, dim=1)
+        grid_logits = grid_logits.view((batch_size, *grid_size)).float()
 
         return grid_logits
 
+
 class FourierEmbedder(nn.Module):
     """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
     each feature dimension of `x[..., i]` into:
@@ -552,11 +175,13 @@ class FourierEmbedder(nn.Module):
         else:
             return x
 
+
 class CrossAttentionProcessor:
     def __call__(self, attn, q, k, v):
-        out = comfy.ops.scaled_dot_product_attention(q, k, v)
+        out = F.scaled_dot_product_attention(q, k, v)
         return out
 
+
 class DropPath(nn.Module):
     """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
     """
@@ -607,42 +232,39 @@ class MLP(nn.Module):
     def forward(self, x):
         return self.drop_path(self.c_proj(self.gelu(self.c_fc(x))))
 
+
 class QKVMultiheadCrossAttention(nn.Module):
     def __init__(
         self,
+        *,
         heads: int,
-        n_data = None,
         width=None,
         qk_norm=False,
         norm_layer=ops.LayerNorm
     ):
         super().__init__()
         self.heads = heads
-        self.n_data = n_data
         self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
         self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
 
-    def forward(self, q, kv):
+        self.attn_processor = CrossAttentionProcessor()
 
+    def forward(self, q, kv):
         _, n_ctx, _ = q.shape
         bs, n_data, width = kv.shape
-
         attn_ch = width // self.heads // 2
         q = q.view(bs, n_ctx, self.heads, -1)
-
         kv = kv.view(bs, n_data, self.heads, -1)
         k, v = torch.split(kv, attn_ch, dim=-1)
 
         q = self.q_norm(q)
         k = self.k_norm(k)
-
-        q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]
-        out = F.scaled_dot_product_attention(q, k, v)
-
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        out = self.attn_processor(self, q, k, v)
         out = out.transpose(1, 2).reshape(bs, n_ctx, -1)
-
         return out
 
+
 class MultiheadCrossAttention(nn.Module):
     def __init__(
         self,
@@ -684,6 +306,7 @@ class MultiheadCrossAttention(nn.Module):
         x = self.c_proj(x)
         return x
 
+
 class ResidualCrossAttentionBlock(nn.Module):
     def __init__(
         self,
@@ -743,7 +366,7 @@ class QKVMultiheadAttention(nn.Module):
         q = self.q_norm(q)
         k = self.k_norm(k)
 
-        q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)]
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
         out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
         return out
 
@@ -760,7 +383,8 @@ class MultiheadAttention(nn.Module):
         drop_path_rate: float = 0.0
     ):
         super().__init__()
-
+        self.width = width
+        self.heads = heads
         self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias)
         self.c_proj = ops.Linear(width, width)
         self.attention = QKVMultiheadAttention(
@@ -867,7 +491,7 @@ class CrossAttentionDecoder(nn.Module):
         self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width)
         if self.downsample_ratio != 1:
             self.latents_proj = ops.Linear(width * downsample_ratio, width)
-        if not self.enable_ln_post:
+        if self.enable_ln_post == False:
             qk_norm = False
         self.cross_attn_decoder = ResidualCrossAttentionBlock(
             width=width,
@@ -898,44 +522,28 @@ class CrossAttentionDecoder(nn.Module):
 
 class ShapeVAE(nn.Module):
     def __init__(
-            self,
-            *,
-            num_latents: int = 4096,
-            embed_dim: int = 64,
-            width: int = 1024,
-            heads: int = 16,
-            num_decoder_layers: int = 16,
-            num_encoder_layers: int = 8,
-            pc_size: int = 81920,
-            pc_sharpedge_size: int = 0,
-            point_feats: int = 4,
-            downsample_ratio: int = 20,
-            geo_decoder_downsample_ratio: int = 1,
-            geo_decoder_mlp_expand_ratio: int = 4,
-            geo_decoder_ln_post: bool = True,
-            num_freqs: int = 8,
-            qkv_bias: bool = False,
-            qk_norm: bool = True,
-            drop_path_rate: float = 0.0,
-            include_pi: bool = False,
-            scale_factor: float = 1.0039506158752403,
-            label_type: str = "binary",
+        self,
+        *,
+        embed_dim: int,
+        width: int,
+        heads: int,
+        num_decoder_layers: int,
+        geo_decoder_downsample_ratio: int = 1,
+        geo_decoder_mlp_expand_ratio: int = 4,
+        geo_decoder_ln_post: bool = True,
+        num_freqs: int = 8,
+        include_pi: bool = True,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        label_type: str = "binary",
+        drop_path_rate: float = 0.0,
+        scale_factor: float = 1.0,
     ):
         super().__init__()
         self.geo_decoder_ln_post = geo_decoder_ln_post
 
         self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
 
-        self.encoder = PointCrossAttention(layers = num_encoder_layers,
-                                    num_latents = num_latents,
-                                    downsample_ratio = downsample_ratio,
-                                    heads = heads,
-                                    pc_size = pc_size,
-                                    width = width,
-                                    point_feats = point_feats,
-                                    fourier_embedder = self.fourier_embedder,
-                                    pc_sharpedge_size = pc_sharpedge_size)
-
         self.post_kl = ops.Linear(embed_dim, width)
 
         self.transformer = Transformer(
@@ -975,14 +583,5 @@ class ShapeVAE(nn.Module):
         grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar)
         return grid_logits.movedim(-2, -1)
 
-    def encode(self, surface):
-
-        pc, feats = surface[:, :, :3], surface[:, :, 3:]
-        latents = self.encoder(pc, feats)
-
-        moments = self.pre_kl(latents)
-        posterior = DiagonalGaussianDistribution(moments, feature_dim = -1)
-
-        latents = posterior.sample()
-
-        return latents
+    def encode(self, x):
+        return None

comfy/ldm/hunyuan3dv2_1/hunyuandit.py

@@ -1,659 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.attention import optimized_attention
-import comfy.model_management
-
-class GELU(nn.Module):
-
-    def __init__(self, dim_in: int, dim_out: int, operations, device, dtype):
-        super().__init__()
-        self.proj = operations.Linear(dim_in, dim_out, device = device, dtype = dtype)
-
-    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
-
-        if gate.device.type == "mps":
-            return F.gelu(gate.to(dtype = torch.float32)).to(dtype = gate.dtype)
-
-        return F.gelu(gate)
-
-    def forward(self, hidden_states):
-
-        hidden_states = self.proj(hidden_states)
-        hidden_states = self.gelu(hidden_states)
-
-        return hidden_states
-
-class FeedForward(nn.Module):
-
-    def __init__(self, dim: int, dim_out = None, mult: int = 4,
-                dropout: float = 0.0, inner_dim = None, operations = None, device = None, dtype = None):
-
-        super().__init__()
-        if inner_dim is None:
-            inner_dim = int(dim * mult)
-
-        dim_out = dim_out if dim_out is not None else dim
-
-        act_fn = GELU(dim, inner_dim, operations = operations, device = device, dtype = dtype)
-
-        self.net = nn.ModuleList([])
-        self.net.append(act_fn)
-
-        self.net.append(nn.Dropout(dropout))
-        self.net.append(operations.Linear(inner_dim, dim_out, device = device, dtype = dtype))
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        for module in self.net:
-            hidden_states = module(hidden_states)
-        return hidden_states
-
-class AddAuxLoss(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, x, loss):
-        # do nothing in forward (no computation)
-        ctx.requires_aux_loss = loss.requires_grad
-        ctx.dtype = loss.dtype
-
-        return x
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        # add the aux loss gradients
-        grad_loss = None
-        # put the aux grad the same as the main grad loss
-        # aux grad contributes equally
-        if ctx.requires_aux_loss:
-            grad_loss = torch.ones(1, dtype = ctx.dtype, device = grad_output.device)
-
-        return grad_output, grad_loss
-
-class MoEGate(nn.Module):
-
-    def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01, device = None, dtype = None):
-
-        super().__init__()
-        self.top_k = num_experts_per_tok
-        self.n_routed_experts = num_experts
-
-        self.alpha = aux_loss_alpha
-
-        self.gating_dim = embed_dim
-        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), device = device, dtype = dtype))
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-
-        # flatten hidden states
-        hidden_states = hidden_states.view(-1, hidden_states.size(-1))
-
-        # get logits and pass it to softmax
-        logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), bias = None)
-        scores = logits.softmax(dim = -1)
-
-        topk_weight, topk_idx = torch.topk(scores, k = self.top_k, dim = -1, sorted = False)
-
-        if self.training and self.alpha > 0.0:
-            scores_for_aux = scores
-
-            # used bincount instead of one hot encoding
-            counts = torch.bincount(topk_idx.view(-1), minlength = self.n_routed_experts).float()
-            ce = counts / topk_idx.numel()  # normalized expert usage
-
-            # mean expert score
-            Pi = scores_for_aux.mean(0)
-
-            # expert balance loss
-            aux_loss = (Pi * ce * self.n_routed_experts).sum() * self.alpha
-        else:
-            aux_loss = None
-
-        return topk_idx, topk_weight, aux_loss
-
-class MoEBlock(nn.Module):
-    def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, dropout: float = 0.0,
-                 ff_inner_dim: int = None, operations = None, device = None, dtype = None):
-        super().__init__()
-
-        self.moe_top_k = moe_top_k
-        self.num_experts = num_experts
-
-        self.experts = nn.ModuleList([
-            FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype)
-            for _ in range(num_experts)
-        ])
-
-        self.gate = MoEGate(dim, num_experts = num_experts, num_experts_per_tok = moe_top_k, device = device, dtype = dtype)
-        self.shared_experts = FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype)
-
-    def forward(self, hidden_states) -> torch.Tensor:
-
-        identity = hidden_states
-        orig_shape = hidden_states.shape
-        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
-
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        flat_topk_idx = topk_idx.view(-1)
-
-        if self.training:
-
-            hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim = 0)
-            y = torch.empty_like(hidden_states, dtype = hidden_states.dtype)
-
-            for i, expert in enumerate(self.experts):
-                tmp = expert(hidden_states[flat_topk_idx == i])
-                y[flat_topk_idx == i] = tmp.to(hidden_states.dtype)
-
-            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim = 1)
-            y =  y.view(*orig_shape)
-
-            y = AddAuxLoss.apply(y, aux_loss)
-        else:
-            y = self.moe_infer(hidden_states, flat_expert_indices = flat_topk_idx,flat_expert_weights = topk_weight.view(-1, 1)).view(*orig_shape)
-
-        y = y + self.shared_experts(identity)
-
-        return y
-
-    @torch.no_grad()
-    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
-
-        expert_cache = torch.zeros_like(x)
-        idxs = flat_expert_indices.argsort()
-
-        # no need for .numpy().cpu() here
-        tokens_per_expert = flat_expert_indices.bincount().cumsum(0)
-        token_idxs = idxs // self.moe_top_k
-
-        for i, end_idx in enumerate(tokens_per_expert):
-
-            start_idx = 0 if i == 0 else tokens_per_expert[i-1]
-
-            if start_idx == end_idx:
-                continue
-
-            expert = self.experts[i]
-            exp_token_idx = token_idxs[start_idx:end_idx]
-
-            expert_tokens = x[exp_token_idx]
-            expert_out = expert(expert_tokens)
-
-            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
-
-            # use index_add_ with a 1-D index tensor directly avoids building a large [N, D] index map and extra memcopy required by scatter_reduce_
-            # + avoid dtype conversion
-            expert_cache.index_add_(0, exp_token_idx, expert_out)
-
-        return expert_cache
-
-class Timesteps(nn.Module):
-    def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0,
-                 scale: float = 1.0, max_period: int = 10000):
-        super().__init__()
-
-        self.num_channels = num_channels
-        half_dim = num_channels // 2
-
-        # precompute the “inv_freq” vector once
-        exponent = -math.log(max_period) * torch.arange(
-            half_dim, dtype=torch.float32
-        ) / (half_dim - downscale_freq_shift)
-
-        inv_freq = torch.exp(exponent)
-
-        # pad
-        if num_channels % 2 == 1:
-            # we’ll pad a zero at the end of the cos-half
-            inv_freq = torch.cat([inv_freq, inv_freq.new_zeros(1)])
-
-        # register to buffer so it moves with the device
-        self.register_buffer("inv_freq", inv_freq, persistent = False)
-        self.scale = scale
-
-    def forward(self, timesteps: torch.Tensor):
-
-        x = timesteps.float().unsqueeze(1) * self.inv_freq.to(timesteps.device).unsqueeze(0)
-
-
-        # fused CUDA kernels for sin and cos
-        sin_emb = x.sin()
-        cos_emb = x.cos()
-
-        emb = torch.cat([sin_emb, cos_emb], dim = 1)
-
-        # scale factor
-        if self.scale != 1.0:
-            emb = emb * self.scale
-
-        # If we padded inv_freq for odd, emb is already wide enough; otherwise:
-        if emb.shape[1] > self.num_channels:
-            emb = emb[:, :self.num_channels]
-
-        return emb
-
-class TimestepEmbedder(nn.Module):
-    def __init__(self, hidden_size, frequency_embedding_size = 256, cond_proj_dim = None, operations = None, device = None, dtype = None):
-        super().__init__()
-
-        self.mlp = nn.Sequential(
-            operations.Linear(hidden_size, frequency_embedding_size, bias=True, device = device, dtype = dtype),
-            nn.GELU(),
-            operations.Linear(frequency_embedding_size, hidden_size, bias=True, device = device, dtype = dtype),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-        if cond_proj_dim is not None:
-            self.cond_proj = operations.Linear(cond_proj_dim, frequency_embedding_size, bias=False, device = device, dtype = dtype)
-
-        self.time_embed = Timesteps(hidden_size)
-
-    def forward(self, timesteps, condition):
-
-        timestep_embed = self.time_embed(timesteps).type(self.mlp[0].weight.dtype)
-
-        if condition is not None:
-            cond_embed = self.cond_proj(condition)
-            timestep_embed = timestep_embed + cond_embed
-
-        time_conditioned = self.mlp(timestep_embed)
-
-        # for broadcasting with image tokens
-        return time_conditioned.unsqueeze(1)
-
-class MLP(nn.Module):
-    def __init__(self, *, width: int, operations = None, device = None, dtype = None):
-        super().__init__()
-        self.width = width
-        self.fc1 = operations.Linear(width, width * 4, device = device, dtype = dtype)
-        self.fc2 = operations.Linear(width * 4, width, device = device, dtype = dtype)
-        self.gelu = nn.GELU()
-
-    def forward(self, x):
-        return self.fc2(self.gelu(self.fc1(x)))
-
-class CrossAttention(nn.Module):
-    def __init__(
-        self,
-        qdim,
-        kdim,
-        num_heads,
-        qkv_bias=True,
-        qk_norm=False,
-        norm_layer=nn.LayerNorm,
-        use_fp16: bool = False,
-        operations = None,
-        dtype = None,
-        device = None,
-        **kwargs,
-    ):
-        super().__init__()
-        self.qdim = qdim
-        self.kdim = kdim
-
-        self.num_heads = num_heads
-        self.head_dim = self.qdim // num_heads
-
-        self.scale = self.head_dim ** -0.5
-
-        self.to_q = operations.Linear(qdim, qdim, bias=qkv_bias, device = device, dtype = dtype)
-        self.to_k = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype)
-        self.to_v = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype)
-
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        if norm_layer == nn.LayerNorm:
-            norm_layer = operations.LayerNorm
-        else:
-            norm_layer = operations.RMSNorm
-
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.out_proj = operations.Linear(qdim, qdim, bias=True, device = device, dtype = dtype)
-
-    def forward(self, x, y):
-
-        b, s1, _ = x.shape
-        _, s2, _ = y.shape
-
-        y = y.to(next(self.to_k.parameters()).dtype)
-
-        q = self.to_q(x)
-        k = self.to_k(y)
-        v = self.to_v(y)
-
-        kv = torch.cat((k, v), dim=-1)
-        split_size = kv.shape[-1] // self.num_heads // 2
-
-        kv = kv.view(1, -1, self.num_heads, split_size * 2)
-        k, v = torch.split(kv, split_size, dim=-1)
-
-        q = q.view(b, s1, self.num_heads, self.head_dim)
-        k = k.view(b, s2, self.num_heads, self.head_dim)
-        v = v.reshape(b, s2, self.num_heads * self.head_dim)
-
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        x = optimized_attention(
-            q.reshape(b, s1, self.num_heads * self.head_dim),
-            k.reshape(b, s2, self.num_heads * self.head_dim),
-            v,
-            heads=self.num_heads,
-        )
-
-        out = self.out_proj(x)
-
-        return out
-
-class Attention(nn.Module):
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        qkv_bias = True,
-        qk_norm = False,
-        norm_layer = nn.LayerNorm,
-        use_fp16: bool = False,
-        operations = None,
-        device = None,
-        dtype = None
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = self.dim // num_heads
-        self.scale = self.head_dim ** -0.5
-
-        self.to_q = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)
-        self.to_k = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)
-        self.to_v = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype)
-
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        if norm_layer == nn.LayerNorm:
-            norm_layer = operations.LayerNorm
-        else:
-            norm_layer = operations.RMSNorm
-
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity()
-        self.out_proj = operations.Linear(dim, dim, device = device, dtype = dtype)
-
-    def forward(self, x):
-        B, N, _ = x.shape
-
-        query = self.to_q(x)
-        key = self.to_k(x)
-        value = self.to_v(x)
-
-        qkv_combined = torch.cat((query, key, value), dim=-1)
-        split_size = qkv_combined.shape[-1] // self.num_heads // 3
-
-        qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3)
-        query, key, value = torch.split(qkv, split_size, dim=-1)
-
-        query = query.reshape(B, N, self.num_heads, self.head_dim)
-        key = key.reshape(B, N, self.num_heads, self.head_dim)
-        value = value.reshape(B, N, self.num_heads * self.head_dim)
-
-        query = self.q_norm(query)
-        key = self.k_norm(key)
-
-        x = optimized_attention(
-            query.reshape(B, N, self.num_heads * self.head_dim),
-            key.reshape(B, N, self.num_heads * self.head_dim),
-            value,
-            heads=self.num_heads,
-        )
-
-        x = self.out_proj(x)
-        return x
-
-class HunYuanDiTBlock(nn.Module):
-    def __init__(
-        self,
-        hidden_size,
-        c_emb_size,
-        num_heads,
-        text_states_dim=1024,
-        qk_norm=False,
-        norm_layer=nn.LayerNorm,
-        qk_norm_layer=True,
-        qkv_bias=True,
-        skip_connection=True,
-        timested_modulate=False,
-        use_moe: bool = False,
-        num_experts: int = 8,
-        moe_top_k: int = 2,
-        use_fp16: bool = False,
-        operations = None,
-        device = None, dtype = None
-    ):
-        super().__init__()
-
-        # eps can't be 1e-6 in fp16 mode because of numerical stability issues
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        self.norm1 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-
-        self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm,
-                               norm_layer=qk_norm_layer, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations)
-
-        self.norm2 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-
-        self.timested_modulate = timested_modulate
-        if self.timested_modulate:
-            self.default_modulation = nn.Sequential(
-                nn.SiLU(),
-                operations.Linear(c_emb_size, hidden_size, bias=True, device = device, dtype = dtype)
-            )
-
-        self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=qkv_bias,
-                                    qk_norm=qk_norm, norm_layer=qk_norm_layer, use_fp16 = use_fp16,
-                                    device = device, dtype = dtype, operations = operations)
-
-        self.norm3 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-
-        if skip_connection:
-            self.skip_norm = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-            self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, device = device, dtype = dtype)
-        else:
-            self.skip_linear = None
-
-        self.use_moe = use_moe
-
-        if self.use_moe:
-            self.moe = MoEBlock(
-                hidden_size,
-                num_experts = num_experts,
-                moe_top_k = moe_top_k,
-                dropout = 0.0,
-                ff_inner_dim = int(hidden_size * 4.0),
-                device = device, dtype = dtype,
-                operations = operations
-            )
-        else:
-            self.mlp = MLP(width=hidden_size, operations=operations, device = device, dtype = dtype)
-
-    def forward(self, hidden_states, conditioning=None, text_states=None, skip_tensor=None):
-
-        if self.skip_linear is not None:
-            combined = torch.cat([skip_tensor, hidden_states], dim=-1)
-            hidden_states = self.skip_linear(combined)
-            hidden_states = self.skip_norm(hidden_states)
-
-        # self attention
-        if self.timested_modulate:
-            modulation_shift = self.default_modulation(conditioning).unsqueeze(dim=1)
-            hidden_states = hidden_states + modulation_shift
-
-        self_attn_out = self.attn1(self.norm1(hidden_states))
-        hidden_states = hidden_states + self_attn_out
-
-        # cross attention
-        hidden_states = hidden_states + self.attn2(self.norm2(hidden_states), text_states)
-
-        # MLP Layer
-        mlp_input = self.norm3(hidden_states)
-
-        if self.use_moe:
-            hidden_states = hidden_states + self.moe(mlp_input)
-        else:
-            hidden_states = hidden_states + self.mlp(mlp_input)
-
-        return hidden_states
-
-class FinalLayer(nn.Module):
-
-    def __init__(self, final_hidden_size, out_channels, operations, use_fp16: bool = False, device = None, dtype = None):
-        super().__init__()
-
-        if use_fp16:
-            eps = 1.0 / 65504
-        else:
-            eps = 1e-6
-
-        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype)
-        self.linear = operations.Linear(final_hidden_size, out_channels, bias = True, device = device, dtype = dtype)
-
-    def forward(self, x):
-        x = self.norm_final(x)
-        x = x[:, 1:]
-        x = self.linear(x)
-        return x
-
-class HunYuanDiTPlain(nn.Module):
-
-    # init with the defaults values from https://huggingface.co/tencent/Hunyuan3D-2.1/blob/main/hunyuan3d-dit-v2-1/config.yaml
-    def __init__(
-        self,
-        in_channels: int = 64,
-        hidden_size: int = 2048,
-        context_dim: int = 1024,
-        depth: int = 21,
-        num_heads: int = 16,
-        qk_norm: bool = True,
-        qkv_bias: bool = False,
-        num_moe_layers: int = 6,
-        guidance_cond_proj_dim = 2048,
-        norm_type = 'layer',
-        num_experts: int = 8,
-        moe_top_k: int = 2,
-        use_fp16: bool = False,
-        dtype = None,
-        device = None,
-        operations = None,
-        **kwargs
-        ):
-
-        self.dtype = dtype
-
-        super().__init__()
-
-        self.depth = depth
-
-        self.in_channels = in_channels
-        self.out_channels = in_channels
-
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-
-        norm = operations.LayerNorm if norm_type == 'layer' else operations.RMSNorm
-        qk_norm = operations.RMSNorm
-
-        self.context_dim = context_dim
-        self.guidance_cond_proj_dim = guidance_cond_proj_dim
-
-        self.x_embedder = operations.Linear(in_channels, hidden_size, bias = True, device = device, dtype = dtype)
-        self.t_embedder = TimestepEmbedder(hidden_size, hidden_size * 4, cond_proj_dim = guidance_cond_proj_dim, device = device, dtype = dtype, operations = operations)
-
-
-        # HUnYuanDiT Blocks
-        self.blocks = nn.ModuleList([
-            HunYuanDiTBlock(hidden_size=hidden_size,
-                            c_emb_size=hidden_size,
-                            num_heads=num_heads,
-                            text_states_dim=context_dim,
-                            qk_norm=qk_norm,
-                            norm_layer = norm,
-                            qk_norm_layer = qk_norm,
-                            skip_connection=layer > depth // 2,
-                            qkv_bias=qkv_bias,
-                            use_moe=True if depth - layer <= num_moe_layers else False,
-                            num_experts=num_experts,
-                            moe_top_k=moe_top_k,
-                            use_fp16 = use_fp16,
-                            device = device, dtype = dtype, operations = operations)
-            for layer in range(depth)
-        ])
-
-        self.depth = depth
-
-        self.final_layer = FinalLayer(hidden_size, self.out_channels, use_fp16 = use_fp16, operations = operations, device = device, dtype = dtype)
-
-    def forward(self, x, t, context, transformer_options = {}, **kwargs):
-
-        x = x.movedim(-1, -2)
-        uncond_emb, cond_emb = context.chunk(2, dim = 0)
-
-        context = torch.cat([cond_emb, uncond_emb], dim = 0)
-        main_condition = context
-
-        t = 1.0 - t
-
-        time_embedded = self.t_embedder(t, condition = kwargs.get('guidance_cond'))
-
-        x = x.to(dtype = next(self.x_embedder.parameters()).dtype)
-        x_embedded = self.x_embedder(x)
-
-        combined = torch.cat([time_embedded, x_embedded], dim=1)
-
-        def block_wrap(args):
-            return block(
-                args["x"],
-                args["t"],
-                args["cond"],
-                skip_tensor=args.get("skip"),)
-
-        skip_stack = []
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-        for idx, block in enumerate(self.blocks):
-            if idx <= self.depth // 2:
-                skip_input = None
-            else:
-                skip_input = skip_stack.pop()
-
-            if ("block", idx) in blocks_replace:
-
-                combined = blocks_replace[("block", idx)](
-                    {
-                        "x": combined,
-                        "t": time_embedded,
-                        "cond": main_condition,
-                        "skip": skip_input,
-                    },
-                    {"original_block": block_wrap},
-                )
-            else:
-                combined = block(combined, time_embedded, main_condition, skip_tensor=skip_input)
-
-            if idx < self.depth // 2:
-                skip_stack.append(combined)
-
-        output = self.final_layer(combined)
-        output =  output.movedim(-2, -1) * (-1.0)
-
-        cond_emb, uncond_emb = output.chunk(2, dim = 0)
-        return torch.cat([uncond_emb, cond_emb])

comfy/ldm/hunyuan_video/model.py

@@ -1,7 +1,6 @@
 #Based on Flux code because of weird hunyuan video code license.
 
 import torch
-import comfy.patcher_extension
 import comfy.ldm.flux.layers
 import comfy.ldm.modules.diffusionmodules.mmdit
 from comfy.ldm.modules.attention import optimized_attention
@@ -40,8 +39,6 @@ class HunyuanVideoParams:
     patch_size: list
     qkv_bias: bool
     guidance_embed: bool
-    byt5: bool
-    meanflow: bool
 
 
 class SelfAttentionRef(nn.Module):
@@ -80,13 +77,13 @@ class TokenRefinerBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, x, c, mask, transformer_options={}):
+    def forward(self, x, c, mask):
         mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)
 
         norm_x = self.norm1(x)
         qkv = self.self_attn.qkv(norm_x)
         q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)
-        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)
+        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True)
 
         x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
         x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
@@ -117,14 +114,14 @@ class IndividualTokenRefiner(nn.Module):
             ]
         )
 
-    def forward(self, x, c, mask, transformer_options={}):
+    def forward(self, x, c, mask):
         m = None
         if mask is not None:
             m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)
             m = m + m.transpose(2, 3)
 
         for block in self.blocks:
-            x = block(x, c, m, transformer_options=transformer_options)
+            x = block(x, c, m)
         return x
 
 
@@ -152,7 +149,6 @@ class TokenRefiner(nn.Module):
         x,
         timesteps,
         mask,
-        transformer_options={},
     ):
         t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
         # m = mask.float().unsqueeze(-1)
@@ -161,33 +157,9 @@ class TokenRefiner(nn.Module):
 
         c = t + self.c_embedder(c.to(x.dtype))
         x = self.input_embedder(x)
-        x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)
+        x = self.individual_token_refiner(x, c, mask)
         return x
 
-
-class ByT5Mapper(nn.Module):
-    def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.layernorm = operations.LayerNorm(in_dim, dtype=dtype, device=device)
-        self.fc1 = operations.Linear(in_dim, hidden_dim, dtype=dtype, device=device)
-        self.fc2 = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
-        self.fc3 = operations.Linear(out_dim, out_dim1, dtype=dtype, device=device)
-        self.use_res = use_res
-        self.act_fn = nn.GELU()
-
-    def forward(self, x):
-        if self.use_res:
-            res = x
-        x = self.layernorm(x)
-        x = self.fc1(x)
-        x = self.act_fn(x)
-        x = self.fc2(x)
-        x2 = self.act_fn(x)
-        x2 = self.fc3(x2)
-        if self.use_res:
-            x2 = x2 + res
-        return x2
-
 class HunyuanVideo(nn.Module):
     """
     Transformer model for flow matching on sequences.
@@ -212,13 +184,9 @@ class HunyuanVideo(nn.Module):
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
 
-        self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations)
+        self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=True, dtype=dtype, device=device, operations=operations)
         self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
-        if params.vec_in_dim is not None:
-            self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
-        else:
-            self.vector_in = None
-
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
         self.guidance_in = (
             MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
         )
@@ -246,23 +214,6 @@ class HunyuanVideo(nn.Module):
             ]
         )
 
-        if params.byt5:
-            self.byt5_in = ByT5Mapper(
-                in_dim=1472,
-                out_dim=2048,
-                hidden_dim=2048,
-                out_dim1=self.hidden_size,
-                use_res=False,
-                dtype=dtype, device=device, operations=operations
-            )
-        else:
-            self.byt5_in = None
-
-        if params.meanflow:
-            self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
-        else:
-            self.time_r_in = None
-
         if final_layer:
             self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
 
@@ -274,12 +225,10 @@ class HunyuanVideo(nn.Module):
         txt_ids: Tensor,
         txt_mask: Tensor,
         timesteps: Tensor,
-        y: Tensor = None,
-        txt_byt5=None,
+        y: Tensor,
         guidance: Tensor = None,
         guiding_frame_index=None,
         ref_latent=None,
-        disable_time_r=False,
         control=None,
         transformer_options={},
     ) -> Tensor:
@@ -290,14 +239,6 @@ class HunyuanVideo(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
 
-        if (self.time_r_in is not None) and (not disable_time_r):
-            w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0]  # This most likely could be improved
-            if len(w) > 0:
-                timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
-                timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
-                vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
-                vec = (vec + vec_r) / 2
-
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)
             ref_latent = self.img_in(ref_latent)
@@ -308,17 +249,13 @@ class HunyuanVideo(nn.Module):
 
         if guiding_frame_index is not None:
             token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0))
-            if self.vector_in is not None:
-                vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
-                vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)
-            else:
-                vec = torch.cat([(token_replace_vec).unsqueeze(1), (vec).unsqueeze(1)], dim=1)
+            vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
+            vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)
             frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2])
             modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)]
             modulation_dims_txt = [(0, None, 1)]
         else:
-            if self.vector_in is not None:
-                vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+            vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
             modulation_dims = None
             modulation_dims_txt = None
 
@@ -329,13 +266,7 @@ class HunyuanVideo(nn.Module):
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
 
-        txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)
-
-        if self.byt5_in is not None and txt_byt5 is not None:
-            txt_byt5 = self.byt5_in(txt_byt5)
-            txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
-            txt = torch.cat((txt, txt_byt5), dim=1)
-            txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1)
+        txt = self.txt_in(txt, timesteps, txt_mask)
 
         ids = torch.cat((img_ids, txt_ids), dim=1)
         pe = self.pe_embedder(ids)
@@ -353,14 +284,14 @@ class HunyuanVideo(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"])
+                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
             else:
-                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options)
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -375,13 +306,13 @@ class HunyuanVideo(nn.Module):
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"])
                     return out
 
-                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -396,16 +327,12 @@ class HunyuanVideo(nn.Module):
 
         img = self.final_layer(img, vec, modulation_dims=modulation_dims)  # (N, T, patch_size ** 2 * out_channels)
 
-        shape = initial_shape[-len(self.patch_size):]
+        shape = initial_shape[-3:]
         for i in range(len(shape)):
             shape[i] = shape[i] // self.patch_size[i]
         img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)
-        if img.ndim == 8:
-            img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
-            img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
-        else:
-            img = img.permute(0, 3, 1, 4, 2, 5)
-            img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3])
+        img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
+        img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
         return img
 
     def img_ids(self, x):
@@ -420,30 +347,9 @@ class HunyuanVideo(nn.Module):
         img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
         return repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
 
-    def img_ids_2d(self, x):
-        bs, c, h, w = x.shape
-        patch_size = self.patch_size
-        h_len = ((h + (patch_size[0] // 2)) // patch_size[0])
-        w_len = ((w + (patch_size[1] // 2)) // patch_size[1])
-        img_ids = torch.zeros((h_len, w_len, 2), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 0] = img_ids[:, :, 0] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
-        return repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)
-
-    def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
-        bs = x.shape[0]
-        if len(self.patch_size) == 3:
-            img_ids = self.img_ids(x)
-            txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
-        else:
-            img_ids = self.img_ids_2d(x)
-            txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+        bs, c, t, h, w = x.shape
+        img_ids = self.img_ids(x)
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options)
         return out

comfy/ldm/hunyuan_video/vae.py

@@ -1,136 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-
-class PixelShuffle2D(nn.Module):
-    def __init__(self, in_dim, out_dim, op=ops.Conv2d):
-        super().__init__()
-        self.conv = op(in_dim, out_dim >> 2, 3, 1, 1)
-        self.ratio = (in_dim << 2) // out_dim
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        h2, w2 = h >> 1, w >> 1
-        y = self.conv(x).view(b, -1, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, -1, h2, w2)
-        r = x.view(b, c, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, c << 2, h2, w2)
-        return y + r.view(b, y.shape[1], self.ratio, h2, w2).mean(2)
-
-
-class PixelUnshuffle2D(nn.Module):
-    def __init__(self, in_dim, out_dim, op=ops.Conv2d):
-        super().__init__()
-        self.conv = op(in_dim, out_dim << 2, 3, 1, 1)
-        self.scale = (out_dim << 2) // in_dim
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        h2, w2 = h << 1, w << 1
-        y = self.conv(x).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2)
-        r = x.repeat_interleave(self.scale, 1).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2)
-        return y + r
-
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, downsample_match_channel=True, **_):
-        super().__init__()
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-        self.conv_in = ops.Conv2d(in_channels, block_out_channels[0], 3, 1, 1)
-
-        self.down = nn.ModuleList()
-        ch = block_out_channels[0]
-        depth = (ffactor_spatial >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=ops.Conv2d)
-                                        for j in range(num_res_blocks)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch
-                stage.downsample = PixelShuffle2D(ch, nxt, ops.Conv2d)
-                ch = nxt
-            self.down.append(stage)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-
-        self.norm_out = ops.GroupNorm(32, ch, 1e-6, True)
-        self.conv_out = ops.Conv2d(ch, z_channels << 1, 3, 1, 1)
-
-    def forward(self, x):
-        x = self.conv_in(x)
-
-        for stage in self.down:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'downsample'):
-                x = stage.downsample(x)
-
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        b, c, h, w = x.shape
-        grp = c // (self.z_channels << 1)
-        skip = x.view(b, c // grp, grp, h, w).mean(2)
-
-        return self.conv_out(F.silu(self.norm_out(x))) + skip
-
-
-class Decoder(nn.Module):
-    def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, upsample_match_channel=True, **_):
-        super().__init__()
-        block_out_channels = block_out_channels[::-1]
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-
-        ch = block_out_channels[0]
-        self.conv_in = ops.Conv2d(z_channels, ch, 3, 1, 1)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d)
-
-        self.up = nn.ModuleList()
-        depth = (ffactor_spatial >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=ops.Conv2d)
-                                        for j in range(num_res_blocks + 1)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch
-                stage.upsample = PixelUnshuffle2D(ch, nxt, ops.Conv2d)
-                ch = nxt
-            self.up.append(stage)
-
-        self.norm_out = ops.GroupNorm(32, ch, 1e-6, True)
-        self.conv_out = ops.Conv2d(ch, out_channels, 3, 1, 1)
-
-    def forward(self, z):
-        x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        for stage in self.up:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'upsample'):
-                x = stage.upsample(x)
-
-        return self.conv_out(F.silu(self.norm_out(x)))

comfy/ldm/hunyuan_video/vae_refiner.py

@@ -1,301 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
-import comfy.ops
-import comfy.ldm.models.autoencoder
-ops = comfy.ops.disable_weight_init
-
-class RMS_norm(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        shape = (dim, 1, 1, 1)
-        self.scale = dim**0.5
-        self.gamma = nn.Parameter(torch.empty(shape))
-
-    def forward(self, x):
-        return F.normalize(x, dim=1) * self.scale * self.gamma
-
-class DnSmpl(nn.Module):
-    def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
-        super().__init__()
-        fct = 2 * 2 * 2 if tds else 1 * 2 * 2
-        assert oc % fct == 0
-        self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1)
-        self.refiner_vae = refiner_vae
-
-        self.tds = tds
-        self.gs = fct * ic // oc
-
-    def forward(self, x):
-        r1 = 2 if self.tds else 1
-        h = self.conv(x)
-
-        if self.tds and self.refiner_vae:
-            hf = h[:, :, :1, :, :]
-            b, c, f, ht, wd = hf.shape
-            hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)
-            hf = hf.permute(0, 4, 6, 1, 2, 3, 5)
-            hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)
-            hf = torch.cat([hf, hf], dim=1)
-
-            hn = h[:, :, 1:, :, :]
-            b, c, frms, ht, wd = hn.shape
-            nf = frms // r1
-            hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
-            hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
-
-            h = torch.cat([hf, hn], dim=2)
-
-            xf = x[:, :, :1, :, :]
-            b, ci, f, ht, wd = xf.shape
-            xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2)
-            xf = xf.permute(0, 4, 6, 1, 2, 3, 5)
-            xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2)
-            B, C, T, H, W = xf.shape
-            xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2)
-
-            xn = x[:, :, 1:, :, :]
-            b, ci, frms, ht, wd = xn.shape
-            nf = frms // r1
-            xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
-            xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
-            B, C, T, H, W = xn.shape
-            xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
-            sc = torch.cat([xf, xn], dim=2)
-        else:
-            b, c, frms, ht, wd = h.shape
-
-            nf = frms // r1
-            h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
-            h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
-
-            b, ci, frms, ht, wd = x.shape
-            nf = frms // r1
-            sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
-            sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6)
-            sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
-            B, C, T, H, W = sc.shape
-            sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
-
-        return h + sc
-
-
-class UpSmpl(nn.Module):
-    def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
-        super().__init__()
-        fct = 2 * 2 * 2 if tus else 1 * 2 * 2
-        self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
-        self.refiner_vae = refiner_vae
-
-        self.tus = tus
-        self.rp = fct * oc // ic
-
-    def forward(self, x):
-        r1 = 2 if self.tus else 1
-        h = self.conv(x)
-
-        if self.tus and self.refiner_vae:
-            hf = h[:, :, :1, :, :]
-            b, c, f, ht, wd = hf.shape
-            nc = c // (2 * 2)
-            hf = hf.reshape(b, 2, 2, nc, f, ht, wd)
-            hf = hf.permute(0, 3, 4, 5, 1, 6, 2)
-            hf = hf.reshape(b, nc, f, ht * 2, wd * 2)
-            hf = hf[:, : hf.shape[1] // 2]
-
-            hn = h[:, :, 1:, :, :]
-            b, c, frms, ht, wd = hn.shape
-            nc = c // (r1 * 2 * 2)
-            hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-
-            h = torch.cat([hf, hn], dim=2)
-
-            xf = x[:, :, :1, :, :]
-            b, ci, f, ht, wd = xf.shape
-            xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1)
-            b, c, f, ht, wd = xf.shape
-            nc = c // (2 * 2)
-            xf = xf.reshape(b, 2, 2, nc, f, ht, wd)
-            xf = xf.permute(0, 3, 4, 5, 1, 6, 2)
-            xf = xf.reshape(b, nc, f, ht * 2, wd * 2)
-
-            xn = x[:, :, 1:, :, :]
-            xn = xn.repeat_interleave(repeats=self.rp, dim=1)
-            b, c, frms, ht, wd = xn.shape
-            nc = c // (r1 * 2 * 2)
-            xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-            sc = torch.cat([xf, xn], dim=2)
-        else:
-            b, c, frms, ht, wd = h.shape
-            nc = c // (r1 * 2 * 2)
-            h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-
-            sc = x.repeat_interleave(repeats=self.rp, dim=1)
-            b, c, frms, ht, wd = sc.shape
-            nc = c // (r1 * 2 * 2)
-            sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd)
-            sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3)
-            sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2)
-
-        return h + sc
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
-        super().__init__()
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-        self.ffactor_temporal = ffactor_temporal
-
-        self.refiner_vae = refiner_vae
-        if self.refiner_vae:
-            conv_op = VideoConv3d
-            norm_op = RMS_norm
-        else:
-            conv_op = ops.Conv3d
-            norm_op = Normalize
-
-        self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1)
-
-        self.down = nn.ModuleList()
-        ch = block_out_channels[0]
-        depth = (ffactor_spatial >> 1).bit_length()
-        depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=conv_op, norm_op=norm_op)
-                                        for j in range(num_res_blocks)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch
-                stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
-                ch = nxt
-            self.down.append(stage)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-
-        self.norm_out = norm_op(ch)
-        self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
-
-        self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer()
-
-    def forward(self, x):
-        if not self.refiner_vae and x.shape[2] == 1:
-            x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
-
-        x = self.conv_in(x)
-
-        for stage in self.down:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'downsample'):
-                x = stage.downsample(x)
-
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        b, c, t, h, w = x.shape
-        grp = c // (self.z_channels << 1)
-        skip = x.view(b, c // grp, grp, t, h, w).mean(2)
-
-        out = self.conv_out(F.silu(self.norm_out(x))) + skip
-
-        if self.refiner_vae:
-            out = self.regul(out)[0]
-
-            out = torch.cat((out[:, :, :1], out), dim=2)
-            out = out.permute(0, 2, 1, 3, 4)
-            b, f_times_2, c, h, w = out.shape
-            out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
-            out = out.permute(0, 2, 1, 3, 4).contiguous()
-
-        return out
-
-class Decoder(nn.Module):
-    def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,
-                 ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_):
-        super().__init__()
-        block_out_channels = block_out_channels[::-1]
-        self.z_channels = z_channels
-        self.block_out_channels = block_out_channels
-        self.num_res_blocks = num_res_blocks
-
-        self.refiner_vae = refiner_vae
-        if self.refiner_vae:
-            conv_op = VideoConv3d
-            norm_op = RMS_norm
-        else:
-            conv_op = ops.Conv3d
-            norm_op = Normalize
-
-        ch = block_out_channels[0]
-        self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
-
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-
-        self.up = nn.ModuleList()
-        depth = (ffactor_spatial >> 1).bit_length()
-        depth_temporal = (ffactor_temporal >> 1).bit_length()
-
-        for i, tgt in enumerate(block_out_channels):
-            stage = nn.Module()
-            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
-                                                     out_channels=tgt,
-                                                     temb_channels=0,
-                                                     conv_op=conv_op, norm_op=norm_op)
-                                        for j in range(num_res_blocks + 1)])
-            ch = tgt
-            if i < depth:
-                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch
-                stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
-                ch = nxt
-            self.up.append(stage)
-
-        self.norm_out = norm_op(ch)
-        self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
-
-    def forward(self, z):
-        if self.refiner_vae:
-            z = z.permute(0, 2, 1, 3, 4)
-            b, f, c, h, w = z.shape
-            z = z.reshape(b, f, 2, c // 2, h, w)
-            z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
-            z = z.permute(0, 2, 1, 3, 4)
-            z = z[:, :, 1:]
-
-        x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
-        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
-
-        for stage in self.up:
-            for blk in stage.block:
-                x = blk(x)
-            if hasattr(stage, 'upsample'):
-                x = stage.upsample(x)
-
-        out = self.conv_out(F.silu(self.norm_out(x)))
-
-        if not self.refiner_vae:
-            if z.shape[-3] == 1:
-                out = out[:, :, -1:]
-
-        return out

comfy/ldm/lightricks/model.py

@@ -1,6 +1,5 @@
 import torch
 from torch import nn
-import comfy.patcher_extension
 import comfy.ldm.modules.attention
 import comfy.ldm.common_dit
 from einops import rearrange
@@ -262,8 +261,8 @@ class CrossAttention(nn.Module):
         self.heads = heads
         self.dim_head = dim_head
 
-        self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)
-        self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device)
+        self.q_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device)
+        self.k_norm = operations.RMSNorm(inner_dim, dtype=dtype, device=device)
 
         self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)
         self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
@@ -271,7 +270,7 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
-    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, mask=None, pe=None):
         q = self.to_q(x)
         context = x if context is None else context
         k = self.to_k(context)
@@ -285,9 +284,9 @@ class CrossAttention(nn.Module):
             k = apply_rotary_emb(k, pe)
 
         if mask is None:
-            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
         else:
-            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
         return self.to_out(out)
 
 
@@ -303,12 +302,12 @@ class BasicTransformerBlock(nn.Module):
 
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
-        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
 
-        x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
+        x += self.attn2(x, context=context, mask=attention_mask)
 
         y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
         x += self.ff(y) * gate_mlp
@@ -421,13 +420,6 @@ class LTXVModel(torch.nn.Module):
         self.patchifier = SymmetricPatchifier(1)
 
     def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, **kwargs)
-
-    def _forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs):
         patches_replace = transformer_options.get("patches_replace", {})
 
         orig_shape = list(x.shape)
@@ -479,10 +471,10 @@ class LTXVModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
                 x = out["img"]
             else:
                 x = block(
@@ -490,8 +482,7 @@ class LTXVModel(torch.nn.Module):
                     context=context,
                     attention_mask=attention_mask,
                     timestep=timestep,
-                    pe=pe,
-                    transformer_options=transformer_options,
+                    pe=pe
                 )
 
         # 3. Output

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -973,7 +973,7 @@ class VideoVAE(nn.Module):
             norm_layer=config.get("norm_layer", "group_norm"),
             causal=config.get("causal_decoder", False),
             timestep_conditioning=self.timestep_conditioning,
-            spatial_padding_mode=config.get("spatial_padding_mode", "reflect"),
+            spatial_padding_mode=config.get("spatial_padding_mode", "zeros"),
         )
 
         self.per_channel_statistics = processor()

comfy/ldm/lumina/model.py

@@ -11,7 +11,6 @@ import comfy.ldm.common_dit
 from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder
 from comfy.ldm.modules.attention import optimized_attention_masked
 from comfy.ldm.flux.layers import EmbedND
-import comfy.patcher_extension
 
 
 def modulate(x, scale):
@@ -104,7 +103,6 @@ class JointAttention(nn.Module):
         x: torch.Tensor,
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
-        transformer_options={},
     ) -> torch.Tensor:
         """
 
@@ -141,7 +139,7 @@ class JointAttention(nn.Module):
         if n_rep >= 1:
             xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
             xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
-        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
 
         return self.out(output)
 
@@ -269,7 +267,6 @@ class JointTransformerBlock(nn.Module):
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor]=None,
-        transformer_options={},
     ):
         """
         Perform a forward pass through the TransformerBlock.
@@ -292,7 +289,6 @@ class JointTransformerBlock(nn.Module):
                     modulate(self.attention_norm1(x), scale_msa),
                     x_mask,
                     freqs_cis,
-                    transformer_options=transformer_options,
                 )
             )
             x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
@@ -307,7 +303,6 @@ class JointTransformerBlock(nn.Module):
                     self.attention_norm1(x),
                     x_mask,
                     freqs_cis,
-                    transformer_options=transformer_options,
                 )
             )
             x = x + self.ffn_norm2(
@@ -498,7 +493,7 @@ class NextDiT(nn.Module):
         return imgs
 
     def patchify_and_embed(
-        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={}
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
     ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
         bsz = len(x)
         pH = pW = self.patch_size
@@ -558,7 +553,7 @@ class NextDiT(nn.Module):
 
         # refine context
         for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
 
         # refine image
         flat_x = []
@@ -577,7 +572,7 @@ class NextDiT(nn.Module):
         padded_img_embed = self.x_embedder(padded_img_embed)
         padded_img_mask = padded_img_mask.unsqueeze(1)
         for layer in self.noise_refiner:
-            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options)
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
 
         if cap_mask is not None:
             mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
@@ -595,15 +590,8 @@ class NextDiT(nn.Module):
 
         return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
 
-    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
-        ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
-
     # def forward(self, x, t, cap_feats, cap_mask):
-    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
         t = 1.0 - timesteps
         cap_feats = context
         cap_mask = attention_mask
@@ -620,13 +608,12 @@ class NextDiT(nn.Module):
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
-        transformer_options = kwargs.get("transformer_options", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
         freqs_cis = freqs_cis.to(x.device)
 
         for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            x = layer(x, mask, freqs_cis, adaln_input)
 
         x = self.final_layer(x, adaln_input)
         x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]

comfy/ldm/mmaudio/vae/activations.py

@@ -1,120 +0,0 @@
-# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import torch
-from torch import nn, sin, pow
-from torch.nn import Parameter
-import comfy.model_management
-
-class Snake(nn.Module):
-    '''
-    Implementation of a sine-based periodic activation function
-    Shape:
-        - Input: (B, C, T)
-        - Output: (B, C, T), same shape as the input
-    Parameters:
-        - alpha - trainable parameter
-    References:
-        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
-        https://arxiv.org/abs/2006.08195
-    Examples:
-        >>> a1 = snake(256)
-        >>> x = torch.randn(256)
-        >>> x = a1(x)
-    '''
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
-        '''
-        Initialization.
-        INPUT:
-            - in_features: shape of the input
-            - alpha: trainable parameter
-            alpha is initialized to 1 by default, higher values = higher-frequency.
-            alpha will be trained along with the rest of your model.
-        '''
-        super(Snake, self).__init__()
-        self.in_features = in_features
-
-        # initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale:
-            self.alpha = Parameter(torch.empty(in_features))
-        else:
-            self.alpha = Parameter(torch.empty(in_features))
-
-        self.alpha.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        '''
-        Forward pass of the function.
-        Applies the function to the input elementwise.
-        Snake ∶= x + 1/a * sin^2 (xa)
-        '''
-        alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
-
-        return x
-
-
-class SnakeBeta(nn.Module):
-    '''
-    A modified Snake function which uses separate parameters for the magnitude of the periodic components
-    Shape:
-        - Input: (B, C, T)
-        - Output: (B, C, T), same shape as the input
-    Parameters:
-        - alpha - trainable parameter that controls frequency
-        - beta - trainable parameter that controls magnitude
-    References:
-        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
-        https://arxiv.org/abs/2006.08195
-    Examples:
-        >>> a1 = snakebeta(256)
-        >>> x = torch.randn(256)
-        >>> x = a1(x)
-    '''
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
-        '''
-        Initialization.
-        INPUT:
-            - in_features: shape of the input
-            - alpha - trainable parameter that controls frequency
-            - beta - trainable parameter that controls magnitude
-            alpha is initialized to 1 by default, higher values = higher-frequency.
-            beta is initialized to 1 by default, higher values = higher-magnitude.
-            alpha will be trained along with the rest of your model.
-        '''
-        super(SnakeBeta, self).__init__()
-        self.in_features = in_features
-
-        # initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale:
-            self.alpha = Parameter(torch.empty(in_features))
-            self.beta = Parameter(torch.empty(in_features))
-        else:
-            self.alpha = Parameter(torch.empty(in_features))
-            self.beta = Parameter(torch.empty(in_features))
-
-        self.alpha.requires_grad = alpha_trainable
-        self.beta.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 0.000000001
-
-    def forward(self, x):
-        '''
-        Forward pass of the function.
-        Applies the function to the input elementwise.
-        SnakeBeta ∶= x + 1/b * sin^2 (xa)
-        '''
-        alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
-        beta = comfy.model_management.cast_to(self.beta, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1)
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-            beta = torch.exp(beta)
-        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
-
-        return x

comfy/ldm/mmaudio/vae/alias_free_torch.py

@@ -1,157 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-import comfy.model_management
-
-if 'sinc' in dir(torch):
-    sinc = torch.sinc
-else:
-    # This code is adopted from adefossez's julius.core.sinc under the MIT License
-    # https://adefossez.github.io/julius/julius/core.html
-    #   LICENSE is in incl_licenses directory.
-    def sinc(x: torch.Tensor):
-        """
-        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
-        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
-        """
-        return torch.where(x == 0,
-                           torch.tensor(1., device=x.device, dtype=x.dtype),
-                           torch.sin(math.pi * x) / math.pi / x)
-
-
-# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
-# https://adefossez.github.io/julius/julius/lowpass.html
-#   LICENSE is in incl_licenses directory.
-def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
-    even = (kernel_size % 2 == 0)
-    half_size = kernel_size // 2
-
-    #For kaiser window
-    delta_f = 4 * half_width
-    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
-    if A > 50.:
-        beta = 0.1102 * (A - 8.7)
-    elif A >= 21.:
-        beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
-    else:
-        beta = 0.
-    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
-
-    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
-    if even:
-        time = (torch.arange(-half_size, half_size) + 0.5)
-    else:
-        time = torch.arange(kernel_size) - half_size
-    if cutoff == 0:
-        filter_ = torch.zeros_like(time)
-    else:
-        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
-        # Normalize filter to have sum = 1, otherwise we will have a small leakage
-        # of the constant component in the input signal.
-        filter_ /= filter_.sum()
-        filter = filter_.view(1, 1, kernel_size)
-
-    return filter
-
-
-class LowPassFilter1d(nn.Module):
-    def __init__(self,
-                 cutoff=0.5,
-                 half_width=0.6,
-                 stride: int = 1,
-                 padding: bool = True,
-                 padding_mode: str = 'replicate',
-                 kernel_size: int = 12):
-        # kernel_size should be even number for stylegan3 setup,
-        # in this implementation, odd number is also possible.
-        super().__init__()
-        if cutoff < -0.:
-            raise ValueError("Minimum cutoff must be larger than zero.")
-        if cutoff > 0.5:
-            raise ValueError("A cutoff above 0.5 does not make sense.")
-        self.kernel_size = kernel_size
-        self.even = (kernel_size % 2 == 0)
-        self.pad_left = kernel_size // 2 - int(self.even)
-        self.pad_right = kernel_size // 2
-        self.stride = stride
-        self.padding = padding
-        self.padding_mode = padding_mode
-        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
-        self.register_buffer("filter", filter)
-
-    #input [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        if self.padding:
-            x = F.pad(x, (self.pad_left, self.pad_right),
-                      mode=self.padding_mode)
-        out = F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device),
-                       stride=self.stride, groups=C)
-
-        return out
-
-
-class UpSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.stride = ratio
-        self.pad = self.kernel_size // ratio - 1
-        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
-        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
-        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
-                                      half_width=0.6 / ratio,
-                                      kernel_size=self.kernel_size)
-        self.register_buffer("filter", filter)
-
-    # x: [B, C, T]
-    def forward(self, x):
-        _, C, _ = x.shape
-
-        x = F.pad(x, (self.pad, self.pad), mode='replicate')
-        x = self.ratio * F.conv_transpose1d(
-            x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)
-        x = x[..., self.pad_left:-self.pad_right]
-
-        return x
-
-
-class DownSample1d(nn.Module):
-    def __init__(self, ratio=2, kernel_size=None):
-        super().__init__()
-        self.ratio = ratio
-        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
-        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
-                                       half_width=0.6 / ratio,
-                                       stride=ratio,
-                                       kernel_size=self.kernel_size)
-
-    def forward(self, x):
-        xx = self.lowpass(x)
-
-        return xx
-
-class Activation1d(nn.Module):
-    def __init__(self,
-                 activation,
-                 up_ratio: int = 2,
-                 down_ratio: int = 2,
-                 up_kernel_size: int = 12,
-                 down_kernel_size: int = 12):
-        super().__init__()
-        self.up_ratio = up_ratio
-        self.down_ratio = down_ratio
-        self.act = activation
-        self.upsample = UpSample1d(up_ratio, up_kernel_size)
-        self.downsample = DownSample1d(down_ratio, down_kernel_size)
-
-    # x: [B,C,T]
-    def forward(self, x):
-        x = self.upsample(x)
-        x = self.act(x)
-        x = self.downsample(x)
-
-        return x

comfy/ldm/mmaudio/vae/autoencoder.py

@@ -1,156 +0,0 @@
-from typing import Literal
-
-import torch
-import torch.nn as nn
-
-from .distributions import DiagonalGaussianDistribution
-from .vae import VAE_16k
-from .bigvgan import BigVGANVocoder
-import logging
-
-try:
-    import torchaudio
-except:
-    logging.warning("torchaudio missing, MMAudio VAE model will be broken")
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
-    return norm_fn(torch.clamp(x, min=clip_val) * C)
-
-
-def spectral_normalize_torch(magnitudes, norm_fn):
-    output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
-    return output
-
-class MelConverter(nn.Module):
-
-    def __init__(
-        self,
-        *,
-        sampling_rate: float,
-        n_fft: int,
-        num_mels: int,
-        hop_size: int,
-        win_size: int,
-        fmin: float,
-        fmax: float,
-        norm_fn,
-    ):
-        super().__init__()
-        self.sampling_rate = sampling_rate
-        self.n_fft = n_fft
-        self.num_mels = num_mels
-        self.hop_size = hop_size
-        self.win_size = win_size
-        self.fmin = fmin
-        self.fmax = fmax
-        self.norm_fn = norm_fn
-
-        # mel = librosa_mel_fn(sr=self.sampling_rate,
-        #                      n_fft=self.n_fft,
-        #                      n_mels=self.num_mels,
-        #                      fmin=self.fmin,
-        #                      fmax=self.fmax)
-        # mel_basis = torch.from_numpy(mel).float()
-        mel_basis = torch.empty((num_mels, 1 + n_fft // 2))
-        hann_window = torch.hann_window(self.win_size)
-
-        self.register_buffer('mel_basis', mel_basis)
-        self.register_buffer('hann_window', hann_window)
-
-    @property
-    def device(self):
-        return self.mel_basis.device
-
-    def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
-        waveform = waveform.clamp(min=-1., max=1.).to(self.device)
-
-        waveform = torch.nn.functional.pad(
-            waveform.unsqueeze(1),
-            [int((self.n_fft - self.hop_size) / 2),
-             int((self.n_fft - self.hop_size) / 2)],
-            mode='reflect')
-        waveform = waveform.squeeze(1)
-
-        spec = torch.stft(waveform,
-                          self.n_fft,
-                          hop_length=self.hop_size,
-                          win_length=self.win_size,
-                          window=self.hann_window,
-                          center=center,
-                          pad_mode='reflect',
-                          normalized=False,
-                          onesided=True,
-                          return_complex=True)
-
-        spec = torch.view_as_real(spec)
-        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
-        spec = torch.matmul(self.mel_basis, spec)
-        spec = spectral_normalize_torch(spec, self.norm_fn)
-
-        return spec
-
-class AudioAutoencoder(nn.Module):
-
-    def __init__(
-        self,
-        *,
-        # ckpt_path: str,
-        mode=Literal['16k', '44k'],
-        need_vae_encoder: bool = True,
-    ):
-        super().__init__()
-
-        assert mode == "16k", "Only 16k mode is supported currently."
-        self.mel_converter = MelConverter(sampling_rate=16_000,
-                            n_fft=1024,
-                            num_mels=80,
-                            hop_size=256,
-                            win_size=1024,
-                            fmin=0,
-                            fmax=8_000,
-                            norm_fn=torch.log10)
-
-        self.vae = VAE_16k().eval()
-
-        bigvgan_config = {
-            "resblock": "1",
-            "num_mels": 80,
-            "upsample_rates": [4, 4, 2, 2, 2, 2],
-            "upsample_kernel_sizes": [8, 8, 4, 4, 4, 4],
-            "upsample_initial_channel": 1536,
-            "resblock_kernel_sizes": [3, 7, 11],
-            "resblock_dilation_sizes": [
-                [1, 3, 5],
-                [1, 3, 5],
-                [1, 3, 5],
-            ],
-            "activation": "snakebeta",
-            "snake_logscale": True,
-        }
-
-        self.vocoder = BigVGANVocoder(
-            bigvgan_config
-        ).eval()
-
-    @torch.inference_mode()
-    def encode_audio(self, x) -> DiagonalGaussianDistribution:
-        # x: (B * L)
-        mel = self.mel_converter(x)
-        dist = self.vae.encode(mel)
-
-        return dist
-
-    @torch.no_grad()
-    def decode(self, z):
-        mel_decoded = self.vae.decode(z)
-        audio = self.vocoder(mel_decoded)
-
-        audio = torchaudio.functional.resample(audio, 16000, 44100)
-        return audio
-
-    @torch.no_grad()
-    def encode(self, audio):
-        audio = audio.mean(dim=1)
-        audio = torchaudio.functional.resample(audio, 44100, 16000)
-        dist = self.encode_audio(audio)
-        return dist.mean

comfy/ldm/mmaudio/vae/bigvgan.py

@@ -1,219 +0,0 @@
-# Copyright (c) 2022 NVIDIA CORPORATION.
-#   Licensed under the MIT license.
-
-# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
-#   LICENSE is in incl_licenses directory.
-
-import torch
-import torch.nn as nn
-from types import SimpleNamespace
-from . import activations
-from .alias_free_torch import Activation1d
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-class AMPBlock1(torch.nn.Module):
-
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
-        super(AMPBlock1, self).__init__()
-        self.h = h
-
-        self.convs1 = nn.ModuleList([
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=dilation[0],
-                       padding=get_padding(kernel_size, dilation[0])),
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=dilation[1],
-                       padding=get_padding(kernel_size, dilation[1])),
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=dilation[2],
-                       padding=get_padding(kernel_size, dilation[2]))
-        ])
-
-        self.convs2 = nn.ModuleList([
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=1,
-                       padding=get_padding(kernel_size, 1)),
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=1,
-                       padding=get_padding(kernel_size, 1)),
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=1,
-                       padding=get_padding(kernel_size, 1))
-        ])
-
-        self.num_layers = len(self.convs1) + len(self.convs2)  # total number of conv layers
-
-        if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
-                for _ in range(self.num_layers)
-            ])
-        elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
-                for _ in range(self.num_layers)
-            ])
-        else:
-            raise NotImplementedError(
-                "activation incorrectly specified. check the config file and look for 'activation'."
-            )
-
-    def forward(self, x):
-        acts1, acts2 = self.activations[::2], self.activations[1::2]
-        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
-            xt = a1(x)
-            xt = c1(xt)
-            xt = a2(xt)
-            xt = c2(xt)
-            x = xt + x
-
-        return x
-
-
-class AMPBlock2(torch.nn.Module):
-
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
-        super(AMPBlock2, self).__init__()
-        self.h = h
-
-        self.convs = nn.ModuleList([
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=dilation[0],
-                       padding=get_padding(kernel_size, dilation[0])),
-                ops.Conv1d(channels,
-                       channels,
-                       kernel_size,
-                       1,
-                       dilation=dilation[1],
-                       padding=get_padding(kernel_size, dilation[1]))
-        ])
-
-        self.num_layers = len(self.convs)  # total number of conv layers
-
-        if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
-                for _ in range(self.num_layers)
-            ])
-        elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
-            self.activations = nn.ModuleList([
-                Activation1d(
-                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
-                for _ in range(self.num_layers)
-            ])
-        else:
-            raise NotImplementedError(
-                "activation incorrectly specified. check the config file and look for 'activation'."
-            )
-
-    def forward(self, x):
-        for c, a in zip(self.convs, self.activations):
-            xt = a(x)
-            xt = c(xt)
-            x = xt + x
-
-        return x
-
-
-class BigVGANVocoder(torch.nn.Module):
-    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
-    def __init__(self, h):
-        super().__init__()
-        if isinstance(h, dict):
-            h = SimpleNamespace(**h)
-        self.h = h
-
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-
-        # pre conv
-        self.conv_pre = ops.Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)
-
-        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
-        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
-
-        # transposed conv-based upsamplers. does not apply anti-aliasing
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(
-                nn.ModuleList([
-                        ops.ConvTranspose1d(h.upsample_initial_channel // (2**i),
-                                        h.upsample_initial_channel // (2**(i + 1)),
-                                        k,
-                                        u,
-                                        padding=(k - u) // 2)
-                ]))
-
-        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel // (2**(i + 1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
-
-        # post conv
-        if h.activation == "snake":  # periodic nonlinearity with snake function and anti-aliasing
-            activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
-            self.activation_post = Activation1d(activation=activation_post)
-        elif h.activation == "snakebeta":  # periodic nonlinearity with snakebeta function and anti-aliasing
-            activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
-            self.activation_post = Activation1d(activation=activation_post)
-        else:
-            raise NotImplementedError(
-                "activation incorrectly specified. check the config file and look for 'activation'."
-            )
-
-        self.conv_post = ops.Conv1d(ch, 1, 7, 1, padding=3)
-
-
-    def forward(self, x):
-        # pre conv
-        x = self.conv_pre(x)
-
-        for i in range(self.num_upsamples):
-            # upsampling
-            for i_up in range(len(self.ups[i])):
-                x = self.ups[i][i_up](x)
-            # AMP blocks
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-
-        # post conv
-        x = self.activation_post(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x

comfy/ldm/mmaudio/vae/distributions.py

@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
-    def sample(self):
-        raise NotImplementedError()
-
-    def mode(self):
-        raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
-    def __init__(self, value):
-        self.value = value
-
-    def sample(self):
-        return self.value
-
-    def mode(self):
-        return self.value
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = torch.exp(0.5 * self.logvar)
-        self.var = torch.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device)
-
-    def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device)
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
-
-    def nll(self, sample, dims=[1,2,3]):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
-
-    def mode(self):
-        return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, torch.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for torch.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + torch.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
-    )

comfy/ldm/mmaudio/vae/vae.py

@@ -1,358 +0,0 @@
-import logging
-from typing import Optional
-
-import torch
-import torch.nn as nn
-
-from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D,
-                                                 Upsample1D, nonlinearity)
-from .distributions import DiagonalGaussianDistribution
-
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-log = logging.getLogger()
-
-DATA_MEAN_80D = [
-    -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927,
-    -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728,
-    -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131,
-    -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280,
-    -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643,
-    -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436,
-    -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282,
-    -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673
-]
-
-DATA_STD_80D = [
-    1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263,
-    0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194,
-    0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043,
-    0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973,
-    0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939,
-    0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604,
-    1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070
-]
-
-DATA_MEAN_128D = [
-    -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597,
-    -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033,
-    -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157,
-    -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782,
-    -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647,
-    -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795,
-    -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121,
-    -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960,
-    -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712,
-    -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120,
-    -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663,
-    -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628,
-    -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861
-]
-
-DATA_STD_128D = [
-    2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659,
-    2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557,
-    2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182,
-    2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991,
-    2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900,
-    2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817,
-    2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609,
-    2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812,
-    2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451,
-    2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877,
-    2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164
-]
-
-
-class VAE(nn.Module):
-
-    def __init__(
-        self,
-        *,
-        data_dim: int,
-        embed_dim: int,
-        hidden_dim: int,
-    ):
-        super().__init__()
-
-        if data_dim == 80:
-            self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32))
-            self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32))
-        elif data_dim == 128:
-            self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32))
-            self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32))
-
-        self.data_mean = self.data_mean.view(1, -1, 1)
-        self.data_std = self.data_std.view(1, -1, 1)
-
-        self.encoder = Encoder1D(
-            dim=hidden_dim,
-            ch_mult=(1, 2, 4),
-            num_res_blocks=2,
-            attn_layers=[3],
-            down_layers=[0],
-            in_dim=data_dim,
-            embed_dim=embed_dim,
-        )
-        self.decoder = Decoder1D(
-            dim=hidden_dim,
-            ch_mult=(1, 2, 4),
-            num_res_blocks=2,
-            attn_layers=[3],
-            down_layers=[0],
-            in_dim=data_dim,
-            out_dim=data_dim,
-            embed_dim=embed_dim,
-        )
-
-        self.embed_dim = embed_dim
-        # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1)
-        # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1)
-
-        self.initialize_weights()
-
-    def initialize_weights(self):
-        pass
-
-    def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution:
-        if normalize:
-            x = self.normalize(x)
-        moments = self.encoder(x)
-        posterior = DiagonalGaussianDistribution(moments)
-        return posterior
-
-    def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor:
-        dec = self.decoder(z)
-        if unnormalize:
-            dec = self.unnormalize(dec)
-        return dec
-
-    def normalize(self, x: torch.Tensor) -> torch.Tensor:
-        return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device)
-
-    def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
-        return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        sample_posterior: bool = True,
-        rng: Optional[torch.Generator] = None,
-        normalize: bool = True,
-        unnormalize: bool = True,
-    ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]:
-
-        posterior = self.encode(x, normalize=normalize)
-        if sample_posterior:
-            z = posterior.sample(rng)
-        else:
-            z = posterior.mode()
-        dec = self.decode(z, unnormalize=unnormalize)
-        return dec, posterior
-
-    def load_weights(self, src_dict) -> None:
-        self.load_state_dict(src_dict, strict=True)
-
-    @property
-    def device(self) -> torch.device:
-        return next(self.parameters()).device
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    def remove_weight_norm(self):
-        return self
-
-
-class Encoder1D(nn.Module):
-
-    def __init__(self,
-                 *,
-                 dim: int,
-                 ch_mult: tuple[int] = (1, 2, 4, 8),
-                 num_res_blocks: int,
-                 attn_layers: list[int] = [],
-                 down_layers: list[int] = [],
-                 resamp_with_conv: bool = True,
-                 in_dim: int,
-                 embed_dim: int,
-                 double_z: bool = True,
-                 kernel_size: int = 3,
-                 clip_act: float = 256.0):
-        super().__init__()
-        self.dim = dim
-        self.num_layers = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.in_channels = in_dim
-        self.clip_act = clip_act
-        self.down_layers = down_layers
-        self.attn_layers = attn_layers
-        self.conv_in = ops.Conv1d(in_dim, self.dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-
-        in_ch_mult = (1, ) + tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        # downsampling
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_layers):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = dim * in_ch_mult[i_level]
-            block_out = dim * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(
-                    ResnetBlock1D(in_dim=block_in,
-                                  out_dim=block_out,
-                                  kernel_size=kernel_size,
-                                  use_norm=True))
-                block_in = block_out
-                if i_level in attn_layers:
-                    attn.append(AttnBlock1D(block_in))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level in down_layers:
-                down.downsample = Downsample1D(block_in, resamp_with_conv)
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock1D(in_dim=block_in,
-                                         out_dim=block_in,
-                                         kernel_size=kernel_size,
-                                         use_norm=True)
-        self.mid.attn_1 = AttnBlock1D(block_in)
-        self.mid.block_2 = ResnetBlock1D(in_dim=block_in,
-                                         out_dim=block_in,
-                                         kernel_size=kernel_size,
-                                         use_norm=True)
-
-        # end
-        self.conv_out = ops.Conv1d(block_in,
-                                 2 * embed_dim if double_z else embed_dim,
-                                 kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-
-        self.learnable_gain = nn.Parameter(torch.zeros([]))
-
-    def forward(self, x):
-
-        # downsampling
-        h = self.conv_in(x)
-        for i_level in range(self.num_layers):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                h = h.clamp(-self.clip_act, self.clip_act)
-            if i_level in self.down_layers:
-                h = self.down[i_level].downsample(h)
-
-        # middle
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        h = h.clamp(-self.clip_act, self.clip_act)
-
-        # end
-        h = nonlinearity(h)
-        h = self.conv_out(h) * (self.learnable_gain + 1)
-        return h
-
-
-class Decoder1D(nn.Module):
-
-    def __init__(self,
-                 *,
-                 dim: int,
-                 out_dim: int,
-                 ch_mult: tuple[int] = (1, 2, 4, 8),
-                 num_res_blocks: int,
-                 attn_layers: list[int] = [],
-                 down_layers: list[int] = [],
-                 kernel_size: int = 3,
-                 resamp_with_conv: bool = True,
-                 in_dim: int,
-                 embed_dim: int,
-                 clip_act: float = 256.0):
-        super().__init__()
-        self.ch = dim
-        self.num_layers = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.in_channels = in_dim
-        self.clip_act = clip_act
-        self.down_layers = [i + 1 for i in down_layers]  # each downlayer add one
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = dim * ch_mult[self.num_layers - 1]
-
-        # z to block_in
-        self.conv_in = ops.Conv1d(embed_dim, block_in, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
-        self.mid.attn_1 = AttnBlock1D(block_in)
-        self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_layers)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = dim * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True))
-                block_in = block_out
-                if i_level in attn_layers:
-                    attn.append(AttnBlock1D(block_in))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level in self.down_layers:
-                up.upsample = Upsample1D(block_in, resamp_with_conv)
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.conv_out = ops.Conv1d(block_in, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-        self.learnable_gain = nn.Parameter(torch.zeros([]))
-
-    def forward(self, z):
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        h = h.clamp(-self.clip_act, self.clip_act)
-
-        # upsampling
-        for i_level in reversed(range(self.num_layers)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-                h = h.clamp(-self.clip_act, self.clip_act)
-            if i_level in self.down_layers:
-                h = self.up[i_level].upsample(h)
-
-        h = nonlinearity(h)
-        h = self.conv_out(h) * (self.learnable_gain + 1)
-        return h
-
-
-def VAE_16k(**kwargs) -> VAE:
-    return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs)
-
-
-def VAE_44k(**kwargs) -> VAE:
-    return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs)
-
-
-def get_my_vae(name: str, **kwargs) -> VAE:
-    if name == '16k':
-        return VAE_16k(**kwargs)
-    if name == '44k':
-        return VAE_44k(**kwargs)
-    raise ValueError(f'Unknown model: {name}')
-

comfy/ldm/mmaudio/vae/vae_modules.py

@@ -1,121 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import vae_attention
-import math
-import comfy.ops
-ops = comfy.ops.disable_weight_init
-
-def nonlinearity(x):
-    # swish
-    return torch.nn.functional.silu(x) / 0.596
-
-def mp_sum(a, b, t=0.5):
-    return a.lerp(b, t) / math.sqrt((1 - t)**2 + t**2)
-
-def normalize(x, dim=None, eps=1e-4):
-    if dim is None:
-        dim = list(range(1, x.ndim))
-    norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
-    norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel()))
-    return x / norm.to(x.dtype)
-
-class ResnetBlock1D(nn.Module):
-
-    def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):
-        super().__init__()
-        self.in_dim = in_dim
-        out_dim = in_dim if out_dim is None else out_dim
-        self.out_dim = out_dim
-        self.use_conv_shortcut = conv_shortcut
-        self.use_norm = use_norm
-
-        self.conv1 = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-        self.conv2 = ops.Conv1d(out_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-        if self.in_dim != self.out_dim:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
-            else:
-                self.nin_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=1, padding=0, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-
-        # pixel norm
-        if self.use_norm:
-            x = normalize(x, dim=1)
-
-        h = x
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        h = nonlinearity(h)
-        h = self.conv2(h)
-
-        if self.in_dim != self.out_dim:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return mp_sum(x, h, t=0.3)
-
-
-class AttnBlock1D(nn.Module):
-
-    def __init__(self, in_channels, num_heads=1):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.num_heads = num_heads
-        self.qkv = ops.Conv1d(in_channels, in_channels * 3, kernel_size=1, padding=0, bias=False)
-        self.proj_out = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
-        self.optimized_attention = vae_attention()
-
-    def forward(self, x):
-        h = x
-        y = self.qkv(h)
-        y = y.reshape(y.shape[0], -1, 3, y.shape[-1])
-        q, k, v = normalize(y, dim=1).unbind(2)
-
-        h = self.optimized_attention(q, k, v)
-        h = self.proj_out(h)
-
-        return mp_sum(x, h, t=0.3)
-
-
-class Upsample1D(nn.Module):
-
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = ops.Conv1d(in_channels, in_channels, kernel_size=3, padding=1, bias=False)
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact')  # support 3D tensor(B,C,T)
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample1D(nn.Module):
-
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv1 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
-            self.conv2 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
-
-    def forward(self, x):
-
-        if self.with_conv:
-            x = self.conv1(x)
-
-        x = F.avg_pool1d(x, kernel_size=2, stride=2)
-
-        if self.with_conv:
-            x = self.conv2(x)
-
-        return x

comfy/ldm/models/autoencoder.py

@@ -11,7 +11,7 @@ from comfy.ldm.modules.ema import LitEma
 import comfy.ops
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
-    def __init__(self, sample: bool = False):
+    def __init__(self, sample: bool = True):
         super().__init__()
         self.sample = sample
 
@@ -19,19 +19,17 @@ class DiagonalGaussianRegularizer(torch.nn.Module):
         yield from ()
 
     def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        log = dict()
         posterior = DiagonalGaussianDistribution(z)
         if self.sample:
             z = posterior.sample()
         else:
             z = posterior.mode()
-        return z, None
+        kl_loss = posterior.kl()
+        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+        log["kl_loss"] = kl_loss
+        return z, log
 
-class EmptyRegularizer(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
-        return z, None
 
 class AbstractAutoencoder(torch.nn.Module):
     """

comfy/ldm/modules/attention.py

@@ -5,9 +5,8 @@ import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
-from typing import Optional, Any, Callable, Union
+from typing import Optional
 import logging
-import functools
 
 from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -18,45 +17,20 @@ if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
-SAGE_ATTENTION_IS_AVAILABLE = False
-try:
-    from sageattention import sageattn
-    SAGE_ATTENTION_IS_AVAILABLE = True
-except ImportError as e:
-    if model_management.sage_attention_enabled():
-        if e.name == "sageattention":
-            logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
-        else:
-            raise e
+if model_management.sage_attention_enabled():
+    try:
+        from sageattention import sageattn
+    except ModuleNotFoundError:
+        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
         exit(-1)
 
-FLASH_ATTENTION_IS_AVAILABLE = False
-try:
-    from flash_attn import flash_attn_func
-    FLASH_ATTENTION_IS_AVAILABLE = True
-except ImportError:
-    if model_management.flash_attention_enabled():
+if model_management.flash_attention_enabled():
+    try:
+        from flash_attn import flash_attn_func
+    except ModuleNotFoundError:
         logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
         exit(-1)
 
-REGISTERED_ATTENTION_FUNCTIONS = {}
-def register_attention_function(name: str, func: Callable):
-    # avoid replacing existing functions
-    if name not in REGISTERED_ATTENTION_FUNCTIONS:
-        REGISTERED_ATTENTION_FUNCTIONS[name] = func
-    else:
-        logging.warning(f"Attention function {name} already registered, skipping registration.")
-
-def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]:
-    if name == "optimized":
-        return optimized_attention
-    elif name not in REGISTERED_ATTENTION_FUNCTIONS:
-        if default is ...:
-            raise KeyError(f"Attention function {name} not found.")
-        else:
-            return default
-    return REGISTERED_ATTENTION_FUNCTIONS[name]
-
 from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
@@ -114,27 +88,7 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-
-def wrap_attn(func):
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        remove_attn_wrapper_key = False
-        try:
-            if "_inside_attn_wrapper" not in kwargs:
-                transformer_options = kwargs.get("transformer_options", None)
-                remove_attn_wrapper_key = True
-                kwargs["_inside_attn_wrapper"] = True
-                if transformer_options is not None:
-                    if "optimized_attention_override" in transformer_options:
-                        return transformer_options["optimized_attention_override"](func, *args, **kwargs)
-            return func(*args, **kwargs)
-        finally:
-            if remove_attn_wrapper_key:
-                del kwargs["_inside_attn_wrapper"]
-    return wrapper
-
-@wrap_attn
-def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -202,8 +156,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         )
     return out
 
-@wrap_attn
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision, query.dtype)
 
     if skip_reshape:
@@ -273,8 +227,7 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-@wrap_attn
-def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -403,8 +356,7 @@ try:
 except:
     pass
 
-@wrap_attn
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -419,7 +371,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             disabled_xformers = True
 
     if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
 
     if skip_reshape:
         # b h k d -> b k h d
@@ -472,8 +424,8 @@ else:
     #TODO: other GPUs ?
     SDP_BATCH_LIMIT = 2**31
 
-@wrap_attn
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -493,7 +445,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             mask = mask.unsqueeze(1)
 
     if SDP_BATCH_LIMIT >= b:
-        out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
         if not skip_output_reshape:
             out = (
                 out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -506,7 +458,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
                 if mask.shape[0] > 1:
                     m = mask[i : i + SDP_BATCH_LIMIT]
 
-            out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention(
+            out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(
                 q[i : i + SDP_BATCH_LIMIT],
                 k[i : i + SDP_BATCH_LIMIT],
                 v[i : i + SDP_BATCH_LIMIT],
@@ -515,8 +467,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
-@wrap_attn
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
@@ -546,7 +498,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
                 lambda t: t.transpose(1, 2),
                 (q, k, v),
             )
-        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
 
     if tensor_layout == "HND":
         if not skip_output_reshape:
@@ -579,8 +531,8 @@ except AttributeError as error:
                     dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
-@wrap_attn
-def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -600,8 +552,7 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
             mask = mask.unsqueeze(1)
 
     try:
-        if mask is not None:
-            raise RuntimeError("Mask must not be set for Flash attention")
+        assert mask is None
         out = flash_attn_wrapper(
             q.transpose(1, 2),
             k.transpose(1, 2),
@@ -643,19 +594,6 @@ else:
 
 optimized_attention_masked = optimized_attention
 
-
-# register core-supported attention functions
-if SAGE_ATTENTION_IS_AVAILABLE:
-    register_attention_function("sage", attention_sage)
-if FLASH_ATTENTION_IS_AVAILABLE:
-    register_attention_function("flash", attention_flash)
-if model_management.xformers_enabled():
-    register_attention_function("xformers", attention_xformers)
-register_attention_function("pytorch", attention_pytorch)
-register_attention_function("sub_quad", attention_sub_quad)
-register_attention_function("split", attention_split)
-
-
 def optimized_attention_for_device(device, mask=False, small_input=False):
     if small_input:
         if model_management.pytorch_attention_enabled():
@@ -688,7 +626,7 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
-    def forward(self, x, context=None, value=None, mask=None, transformer_options={}):
+    def forward(self, x, context=None, value=None, mask=None):
         q = self.to_q(x)
         context = default(context, x)
         k = self.to_k(context)
@@ -699,9 +637,9 @@ class CrossAttention(nn.Module):
             v = self.to_v(context)
 
         if mask is None:
-            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
         else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
         return self.to_out(out)
 
 
@@ -805,14 +743,14 @@ class BasicTransformerBlock(nn.Module):
             n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
             n = self.attn1.to_out(n)
         else:
-            n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options)
+            n = self.attn1(n, context=context_attn1, value=value_attn1)
 
         if "attn1_output_patch" in transformer_patches:
             patch = transformer_patches["attn1_output_patch"]
             for p in patch:
                 n = p(n, extra_options)
 
-        x = n + x
+        x += n
         if "middle_patch" in transformer_patches:
             patch = transformer_patches["middle_patch"]
             for p in patch:
@@ -845,19 +783,19 @@ class BasicTransformerBlock(nn.Module):
                 n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
                 n = self.attn2.to_out(n)
             else:
-                n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options)
+                n = self.attn2(n, context=context_attn2, value=value_attn2)
 
         if "attn2_output_patch" in transformer_patches:
             patch = transformer_patches["attn2_output_patch"]
             for p in patch:
                 n = p(n, extra_options)
 
-        x = n + x
+        x += n
         if self.is_res:
             x_skip = x
         x = self.ff(self.norm3(x))
         if self.is_res:
-            x = x_skip + x
+            x += x_skip
 
         return x
 
@@ -1076,7 +1014,7 @@ class SpatialVideoTransformer(SpatialTransformer):
 
             B, S, C = x_mix.shape
             x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
-            x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options)
+            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
             x_mix = rearrange(
                 x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
             )

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -109,7 +109,7 @@ class PatchEmbed(nn.Module):
 def modulate(x, shift, scale):
     if shift is None:
         shift = torch.zeros_like(scale)
-    return torch.addcmul(shift.unsqueeze(1), x, 1+ scale.unsqueeze(1))
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
 
 
 #################################################################################
@@ -564,7 +564,10 @@ class DismantledBlock(nn.Module):
         assert not self.pre_only
         attn1 = self.attn.post_attention(attn)
         attn2 = self.attn2.post_attention(attn2)
-        x = gate_cat(x, gate_msa, gate_msa2, attn1, attn2)
+        out1 = gate_msa.unsqueeze(1) * attn1
+        out2 = gate_msa2.unsqueeze(1) * attn2
+        x = x + out1
+        x = x + out2
         x = x + gate_mlp.unsqueeze(1) * self.mlp(
             modulate(self.norm2(x), shift_mlp, scale_mlp)
         )
@@ -591,11 +594,6 @@ class DismantledBlock(nn.Module):
             )
             return self.post_attention(attn, *intermediates)
 
-def gate_cat(x, gate_msa, gate_msa2, attn1, attn2):
-    out1 = gate_msa.unsqueeze(1) * attn1
-    out2 = gate_msa2.unsqueeze(1) * attn2
-    x = torch.stack([x, out1, out2], dim=0).sum(dim=0)
-    return x
 
 def block_mixing(*args, use_checkpoint=True, **kwargs):
     if use_checkpoint:
@@ -606,7 +604,7 @@ def block_mixing(*args, use_checkpoint=True, **kwargs):
         return _block_mixing(*args, **kwargs)
 
 
-def _block_mixing(context, x, context_block, x_block, c, transformer_options={}):
+def _block_mixing(context, x, context_block, x_block, c):
     context_qkv, context_intermediates = context_block.pre_attention(context, c)
 
     if x_block.x_block_self_attn:
@@ -622,7 +620,6 @@ def _block_mixing(context, x, context_block, x_block, c, transformer_options={})
     attn = optimized_attention(
         qkv[0], qkv[1], qkv[2],
         heads=x_block.attn.num_heads,
-        transformer_options=transformer_options,
     )
     context_attn, x_attn = (
         attn[:, : context_qkv[0].shape[1]],
@@ -638,7 +635,6 @@ def _block_mixing(context, x, context_block, x_block, c, transformer_options={})
         attn2 = optimized_attention(
                 x_qkv2[0], x_qkv2[1], x_qkv2[2],
                 heads=x_block.attn2.num_heads,
-                transformer_options=transformer_options,
             )
         x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
     else:
@@ -960,10 +956,10 @@ class MMDiT(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"])
+                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
                 context = out["txt"]
                 x = out["img"]
             else:
@@ -972,7 +968,6 @@ class MMDiT(nn.Module):
                     x,
                     c=c_mod,
                     use_checkpoint=self.use_checkpoint,
-                    transformer_options=transformer_options,
                 )
             if control is not None:
                 control_o = control.get("output")

comfy/ldm/modules/diffusionmodules/model.py

@@ -36,7 +36,7 @@ def get_timestep_embedding(timesteps, embedding_dim):
 
 def nonlinearity(x):
     # swish
-    return torch.nn.functional.silu(x)
+    return x*torch.sigmoid(x)
 
 
 def Normalize(in_channels, num_groups=32):
@@ -145,7 +145,7 @@ class Downsample(nn.Module):
 
 class ResnetBlock(nn.Module):
     def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize):
+                 dropout, temb_channels=512, conv_op=ops.Conv2d):
         super().__init__()
         self.in_channels = in_channels
         out_channels = in_channels if out_channels is None else out_channels
@@ -153,7 +153,7 @@ class ResnetBlock(nn.Module):
         self.use_conv_shortcut = conv_shortcut
 
         self.swish = torch.nn.SiLU(inplace=True)
-        self.norm1 = norm_op(in_channels)
+        self.norm1 = Normalize(in_channels)
         self.conv1 = conv_op(in_channels,
                                      out_channels,
                                      kernel_size=3,
@@ -162,7 +162,7 @@ class ResnetBlock(nn.Module):
         if temb_channels > 0:
             self.temb_proj = ops.Linear(temb_channels,
                                              out_channels)
-        self.norm2 = norm_op(out_channels)
+        self.norm2 = Normalize(out_channels)
         self.dropout = torch.nn.Dropout(dropout, inplace=True)
         self.conv2 = conv_op(out_channels,
                                      out_channels,
@@ -183,7 +183,7 @@ class ResnetBlock(nn.Module):
                                                     stride=1,
                                                     padding=0)
 
-    def forward(self, x, temb=None):
+    def forward(self, x, temb):
         h = x
         h = self.norm1(h)
         h = self.swish(h)
@@ -285,7 +285,7 @@ def pytorch_attention(q, k, v):
     )
 
     try:
-        out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
         out = out.transpose(2, 3).reshape(orig_shape)
     except model_management.OOM_EXCEPTION:
         logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
@@ -305,11 +305,11 @@ def vae_attention():
         return normal_attention
 
 class AttnBlock(nn.Module):
-    def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize):
+    def __init__(self, in_channels, conv_op=ops.Conv2d):
         super().__init__()
         self.in_channels = in_channels
 
-        self.norm = norm_op(in_channels)
+        self.norm = Normalize(in_channels)
         self.q = conv_op(in_channels,
                                  in_channels,
                                  kernel_size=1,

comfy/ldm/modules/sub_quadratic_attention.py

@@ -31,7 +31,7 @@ def dynamic_slice(
     starts: List[int],
     sizes: List[int],
 ) -> Tensor:
-    slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes))
+    slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
     return x[slicing]
 
 class AttnChunk(NamedTuple):

comfy/ldm/omnigen/omnigen2.py

@@ -1,470 +0,0 @@
-# Original code: https://github.com/VectorSpaceLab/OmniGen2
-
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from comfy.ldm.lightricks.model import Timesteps
-from comfy.ldm.flux.layers import EmbedND
-from comfy.ldm.modules.attention import optimized_attention_masked
-import comfy.model_management
-import comfy.ldm.common_dit
-
-
-def apply_rotary_emb(x, freqs_cis):
-    if x.shape[1] == 0:
-        return x
-
-    t_ = x.reshape(*x.shape[:-1], -1, 1, 2)
-    t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
-    return t_out.reshape(*x.shape).to(dtype=x.dtype)
-
-
-def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-    return F.silu(x) * y
-
-
-class TimestepEmbedding(nn.Module):
-    def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device)
-        self.act = nn.SiLU()
-        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device)
-
-    def forward(self, sample: torch.Tensor) -> torch.Tensor:
-        sample = self.linear_1(sample)
-        sample = self.act(sample)
-        sample = self.linear_2(sample)
-        return sample
-
-
-class LuminaRMSNormZero(nn.Module):
-    def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.silu = nn.SiLU()
-        self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device)
-        self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        emb = self.linear(self.silu(emb))
-        scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1)
-        x = self.norm(x) * (1 + scale_msa[:, None])
-        return x, gate_msa, scale_mlp, gate_mlp
-
-
-class LuminaLayerNormContinuous(nn.Module):
-    def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.silu = nn.SiLU()
-        self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device)
-        self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device)
-        self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None
-
-    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
-        emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype))
-        x = self.norm(x) * (1 + emb)[:, None, :]
-        if self.linear_2 is not None:
-            x = self.linear_2(x)
-        return x
-
-
-class LuminaFeedForward(nn.Module):
-    def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None):
-        super().__init__()
-        inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)
-        self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device)
-        self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        h1, h2 = self.linear_1(x), self.linear_3(x)
-        return self.linear_2(swiglu(h1, h2))
-
-
-class Lumina2CombinedTimestepCaptionEmbedding(nn.Module):
-    def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale)
-        self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations)
-        self.caption_embedder = nn.Sequential(
-            operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device),
-            operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device),
-        )
-
-    def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:
-        timestep_proj = self.time_proj(timestep).to(dtype=dtype)
-        time_embed = self.timestep_embedder(timestep_proj)
-        caption_embed = self.caption_embedder(text_hidden_states)
-        return time_embed, caption_embed
-
-
-class Attention(nn.Module):
-    def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.heads = heads
-        self.kv_heads = kv_heads
-        self.dim_head = dim_head
-        self.scale = dim_head ** -0.5
-
-        self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device)
-        self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
-        self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device)
-
-        self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
-        self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
-
-        self.to_out = nn.Sequential(
-            operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device),
-            nn.Dropout(0.0)
-        )
-
-    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
-        batch_size, sequence_length, _ = hidden_states.shape
-
-        query = self.to_q(hidden_states)
-        key = self.to_k(encoder_hidden_states)
-        value = self.to_v(encoder_hidden_states)
-
-        query = query.view(batch_size, -1, self.heads, self.dim_head)
-        key = key.view(batch_size, -1, self.kv_heads, self.dim_head)
-        value = value.view(batch_size, -1, self.kv_heads, self.dim_head)
-
-        query = self.norm_q(query)
-        key = self.norm_k(key)
-
-        if image_rotary_emb is not None:
-            query = apply_rotary_emb(query, image_rotary_emb)
-            key = apply_rotary_emb(key, image_rotary_emb)
-
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        if self.kv_heads < self.heads:
-            key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
-            value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
-
-        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
-        hidden_states = self.to_out[0](hidden_states)
-        return hidden_states
-
-
-class OmniGen2TransformerBlock(nn.Module):
-    def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.modulation = modulation
-
-        self.attn = Attention(
-            query_dim=dim,
-            dim_head=dim // num_attention_heads,
-            heads=num_attention_heads,
-            kv_heads=num_kv_heads,
-            eps=1e-5,
-            bias=False,
-            dtype=dtype, device=device, operations=operations,
-        )
-
-        self.feed_forward = LuminaFeedForward(
-            dim=dim,
-            inner_dim=4 * dim,
-            multiple_of=multiple_of,
-            dtype=dtype, device=device, operations=operations
-        )
-
-        if modulation:
-            self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations)
-        else:
-            self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
-
-        self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
-        self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
-        self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
-
-    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
-        if self.modulation:
-            norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
-            hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
-            mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
-            hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
-        else:
-            norm_hidden_states = self.norm1(hidden_states)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
-            hidden_states = hidden_states + self.norm2(attn_output)
-            mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
-            hidden_states = hidden_states + self.ffn_norm2(mlp_output)
-        return hidden_states
-
-
-class OmniGen2RotaryPosEmbed(nn.Module):
-    def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2):
-        super().__init__()
-        self.theta = theta
-        self.axes_dim = axes_dim
-        self.axes_lens = axes_lens
-        self.patch_size = patch_size
-        self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim)
-
-    def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device):
-        p = self.patch_size
-
-        seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)]
-
-        max_seq_len = max(seq_lengths)
-        max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len])
-        max_img_len = max(l_effective_img_len)
-
-        position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device)
-
-        for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)):
-            position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3")
-
-            pe_shift = cap_seq_len
-            pe_shift_len = cap_seq_len
-
-            if ref_img_sizes[i] is not None:
-                for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]):
-                    H, W = ref_img_size
-                    ref_H_tokens, ref_W_tokens = H // p, W // p
-
-                    row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten()
-                    col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten()
-                    position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift
-                    position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids
-                    position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids
-
-                    pe_shift += max(ref_H_tokens, ref_W_tokens)
-                    pe_shift_len += ref_img_len
-
-            H, W = img_sizes[i]
-            H_tokens, W_tokens = H // p, W // p
-
-            row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten()
-            col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten()
-
-            position_ids[i, pe_shift_len: seq_len, 0] = pe_shift
-            position_ids[i, pe_shift_len: seq_len, 1] = row_ids
-            position_ids[i, pe_shift_len: seq_len, 2] = col_ids
-
-        freqs_cis = self.rope_embedder(position_ids).movedim(1, 2)
-
-        cap_freqs_cis_shape = list(freqs_cis.shape)
-        cap_freqs_cis_shape[1] = encoder_seq_len
-        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        ref_img_freqs_cis_shape = list(freqs_cis.shape)
-        ref_img_freqs_cis_shape[1] = max_ref_img_len
-        ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        img_freqs_cis_shape = list(freqs_cis.shape)
-        img_freqs_cis_shape[1] = max_img_len
-        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)):
-            cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len]
-            ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)]
-            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len]
-
-        return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths
-
-
-class OmniGen2Transformer2DModel(nn.Module):
-    def __init__(
-        self,
-        patch_size: int = 2,
-        in_channels: int = 16,
-        out_channels: Optional[int] = None,
-        hidden_size: int = 2304,
-        num_layers: int = 26,
-        num_refiner_layers: int = 2,
-        num_attention_heads: int = 24,
-        num_kv_heads: int = 8,
-        multiple_of: int = 256,
-        ffn_dim_multiplier: Optional[float] = None,
-        norm_eps: float = 1e-5,
-        axes_dim_rope: Tuple[int, int, int] = (32, 32, 32),
-        axes_lens: Tuple[int, int, int] = (300, 512, 512),
-        text_feat_dim: int = 1024,
-        timestep_scale: float = 1.0,
-        image_model=None,
-        device=None,
-        dtype=None,
-        operations=None,
-    ):
-        super().__init__()
-
-        self.patch_size = patch_size
-        self.out_channels = out_channels or in_channels
-        self.hidden_size = hidden_size
-        self.dtype = dtype
-
-        self.rope_embedder = OmniGen2RotaryPosEmbed(
-            theta=10000,
-            axes_dim=axes_dim_rope,
-            axes_lens=axes_lens,
-            patch_size=patch_size,
-        )
-
-        self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
-        self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device)
-
-        self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding(
-            hidden_size=hidden_size,
-            text_feat_dim=text_feat_dim,
-            norm_eps=norm_eps,
-            timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
-        )
-
-        self.noise_refiner = nn.ModuleList([
-            OmniGen2TransformerBlock(
-                hidden_size, num_attention_heads, num_kv_heads,
-                multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
-            ) for _ in range(num_refiner_layers)
-        ])
-
-        self.ref_image_refiner = nn.ModuleList([
-            OmniGen2TransformerBlock(
-                hidden_size, num_attention_heads, num_kv_heads,
-                multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
-            ) for _ in range(num_refiner_layers)
-        ])
-
-        self.context_refiner = nn.ModuleList([
-            OmniGen2TransformerBlock(
-                hidden_size, num_attention_heads, num_kv_heads,
-                multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations
-            ) for _ in range(num_refiner_layers)
-        ])
-
-        self.layers = nn.ModuleList([
-            OmniGen2TransformerBlock(
-                hidden_size, num_attention_heads, num_kv_heads,
-                multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations
-            ) for _ in range(num_layers)
-        ])
-
-        self.norm_out = LuminaLayerNormContinuous(
-            embedding_dim=hidden_size,
-            conditioning_embedding_dim=min(hidden_size, 1024),
-            elementwise_affine=False,
-            eps=1e-6,
-            out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations
-        )
-
-        self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype))
-
-    def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states):
-        batch_size = len(hidden_states)
-        p = self.patch_size
-
-        img_sizes = [(img.size(1), img.size(2)) for img in hidden_states]
-        l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes]
-
-        if ref_image_hidden_states is not None:
-            ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states))
-            ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size
-            l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes]
-        else:
-            ref_img_sizes = [None for _ in range(batch_size)]
-            l_effective_ref_img_len = [[0] for _ in range(batch_size)]
-
-        flat_ref_img_hidden_states = None
-        if ref_image_hidden_states is not None:
-            imgs = []
-            for ref_img in ref_image_hidden_states:
-                B, C, H, W = ref_img.size()
-                ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
-                imgs.append(ref_img)
-            flat_ref_img_hidden_states = torch.cat(imgs, dim=1)
-
-        img = hidden_states
-        B, C, H, W = img.size()
-        flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
-
-        return (
-            flat_hidden_states, flat_ref_img_hidden_states,
-            None, None,
-            l_effective_ref_img_len, l_effective_img_len,
-            ref_img_sizes, img_sizes,
-        )
-
-    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}):
-        batch_size = len(hidden_states)
-
-        hidden_states = self.x_embedder(hidden_states)
-        if ref_image_hidden_states is not None:
-            ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states)
-            image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device)
-
-            for i in range(batch_size):
-                shift = 0
-                for j, ref_img_len in enumerate(l_effective_ref_img_len[i]):
-                    ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j]
-                    shift += ref_img_len
-
-        for layer in self.noise_refiner:
-            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options)
-
-        if ref_image_hidden_states is not None:
-            for layer in self.ref_image_refiner:
-                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options)
-
-            hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)
-
-        return hidden_states
-
-    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs):
-        B, C, H, W = x.shape
-        hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
-        _, _, H_padded, W_padded = hidden_states.shape
-        timestep = 1.0 - timesteps
-        text_hidden_states = context
-        text_attention_mask = attention_mask
-        ref_image_hidden_states = ref_latents
-        device = hidden_states.device
-
-        temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype)
-
-        (
-            hidden_states, ref_image_hidden_states,
-            img_mask, ref_img_mask,
-            l_effective_ref_img_len, l_effective_img_len,
-            ref_img_sizes, img_sizes,
-        ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states)
-
-        (
-            context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb,
-            rotary_emb, encoder_seq_lengths, seq_lengths,
-        ) = self.rope_embedder(
-            hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0],
-            l_effective_ref_img_len, l_effective_img_len,
-            ref_img_sizes, img_sizes, device,
-        )
-
-        for layer in self.context_refiner:
-            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options)
-
-        img_len = hidden_states.shape[1]
-        combined_img_hidden_states = self.img_patch_embed_and_refine(
-            hidden_states, ref_image_hidden_states,
-            img_mask, ref_img_mask,
-            noise_rotary_emb, ref_img_rotary_emb,
-            l_effective_ref_img_len, l_effective_img_len,
-            temb,
-            transformer_options=transformer_options,
-        )
-
-        hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
-        attention_mask = None
-
-        for layer in self.layers:
-            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options)
-
-        hidden_states = self.norm_out(hidden_states, temb)
-
-        p = self.patch_size
-        output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)',  h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W]
-
-        return -output

comfy/ldm/pixart/pixartms.py

@@ -1,256 +1,256 @@
-# Based on:
-# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
-# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
-import torch
-import torch.nn as nn
-
-from .blocks import (
-    t2i_modulate,
-    CaptionEmbedder,
-    AttentionKVCompress,
-    MultiHeadCrossAttention,
-    T2IFinalLayer,
-    SizeEmbedder,
-)
-from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
-
-
-def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
-    grid_h, grid_w = torch.meshgrid(
-        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
-        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
-        indexing='ij'
-    )
-    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
-    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
-    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
-    return emb
-
-class PixArtMSBlock(nn.Module):
-    """
-    A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
-    """
-    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
-                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        self.attn = AttentionKVCompress(
-            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
-            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
-        )
-        self.cross_attn = MultiHeadCrossAttention(
-            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
-        )
-        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
-        # to be compatible with lower version pytorch
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(
-            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
-            dtype=dtype, device=device, operations=operations
-        )
-        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
-
-    def forward(self, x, y, t, mask=None, HW=None, **kwargs):
-        B, N, C = x.shape
-
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)
-        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
-        x = x + self.cross_attn(x, y, mask)
-        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
-
-        return x
-
-
-### Core PixArt Model ###
-class PixArtMS(nn.Module):
-    """
-    Diffusion model with a Transformer backbone.
-    """
-    def __init__(
-            self,
-            input_size=32,
-            patch_size=2,
-            in_channels=4,
-            hidden_size=1152,
-            depth=28,
-            num_heads=16,
-            mlp_ratio=4.0,
-            class_dropout_prob=0.1,
-            learn_sigma=True,
-            pred_sigma=True,
-            drop_path: float = 0.,
-            caption_channels=4096,
-            pe_interpolation=None,
-            pe_precision=None,
-            config=None,
-            model_max_length=120,
-            micro_condition=True,
-            qk_norm=False,
-            kv_compress_config=None,
-            dtype=None,
-            device=None,
-            operations=None,
-            **kwargs,
-    ):
-        nn.Module.__init__(self)
-        self.dtype = dtype
-        self.pred_sigma = pred_sigma
-        self.in_channels = in_channels
-        self.out_channels = in_channels * 2 if pred_sigma else in_channels
-        self.patch_size = patch_size
-        self.num_heads = num_heads
-        self.pe_interpolation = pe_interpolation
-        self.pe_precision = pe_precision
-        self.hidden_size = hidden_size
-        self.depth = depth
-
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.t_block = nn.Sequential(
-            nn.SiLU(),
-            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
-        )
-        self.x_embedder = PatchEmbed(
-            patch_size=patch_size,
-            in_chans=in_channels,
-            embed_dim=hidden_size,
-            bias=True,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-        self.t_embedder = TimestepEmbedder(
-            hidden_size, dtype=dtype, device=device, operations=operations,
-        )
-        self.y_embedder = CaptionEmbedder(
-            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
-            act_layer=approx_gelu, token_num=model_max_length,
-            dtype=dtype, device=device, operations=operations,
-        )
-
-        self.micro_conditioning = micro_condition
-        if self.micro_conditioning:
-            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
-            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
-
-        # For fixed sin-cos embedding:
-        # num_patches = (input_size // patch_size) * (input_size // patch_size)
-        # self.base_size = input_size // self.patch_size
-        # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
-
-        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
-        if kv_compress_config is None:
-            kv_compress_config = {
-                'sampling': None,
-                'scale_factor': 1,
-                'kv_compress_layer': [],
-            }
-        self.blocks = nn.ModuleList([
-            PixArtMSBlock(
-                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
-                sampling=kv_compress_config['sampling'],
-                sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
-                qk_norm=qk_norm,
-                dtype=dtype,
-                device=device,
-                operations=operations,
-            )
-            for i in range(depth)
-        ])
-        self.final_layer = T2IFinalLayer(
-            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
-        )
-
-    def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):
-        """
-        Original forward pass of PixArt.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N, 1, 120, C) conditioning
-        ar: (N, 1): aspect ratio
-        cs: (N ,2) size conditioning for height/width
-        """
-        B, C, H, W = x.shape
-        c_res = (H + W) // 2
-        pe_interpolation = self.pe_interpolation
-        if pe_interpolation is None or self.pe_precision is not None:
-            # calculate pe_interpolation on-the-fly
-            pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0)
-
-        pos_embed = get_2d_sincos_pos_embed_torch(
-            self.hidden_size,
-            h=(H // self.patch_size),
-            w=(W // self.patch_size),
-            pe_interpolation=pe_interpolation,
-            base_size=((round(c_res / 64) * 64) // self.patch_size),
-            device=x.device,
-            dtype=x.dtype,
-        ).unsqueeze(0)
-
-        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep, x.dtype)  # (N, D)
-
-        if self.micro_conditioning and (c_size is not None and c_ar is not None):
-            bs = x.shape[0]
-            c_size = self.csize_embedder(c_size, bs)  # (N, D)
-            c_ar = self.ar_embedder(c_ar, bs)  # (N, D)
-            t = t + torch.cat([c_size, c_ar], dim=1)
-
-        t0 = self.t_block(t)
-        y = self.y_embedder(y, self.training)  # (N, D)
-
-        if mask is not None:
-            if mask.shape[0] != y.shape[0]:
-                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
-            mask = mask.squeeze(1).squeeze(1)
-            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
-            y_lens = mask.sum(dim=1).tolist()
-        else:
-            y_lens = None
-            y = y.squeeze(1).view(1, -1, x.shape[-1])
-        for block in self.blocks:
-            x = block(x, y, t0, y_lens, (H, W), **kwargs)  # (N, T, D)
-
-        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x, H, W)  # (N, out_channels, H, W)
-
-        return x
-
-    def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
-        B, C, H, W = x.shape
-
-        # Fallback for missing microconds
-        if self.micro_conditioning:
-            if c_size is None:
-                c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
-
-            if c_ar is None:
-                c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
-
-        ## Still accepts the input w/o that dim but returns garbage
-        if len(context.shape) == 3:
-            context = context.unsqueeze(1)
-
-        ## run original forward pass
-        out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)
-
-        ## only return EPS
-        if self.pred_sigma:
-            return out[:, :self.in_channels]
-        return out
-
-    def unpatchify(self, x, h, w):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        h = h // self.patch_size
-        w = w // self.patch_size
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
-        return imgs
+# Based on:
+# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
+# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
+import torch
+import torch.nn as nn
+
+from .blocks import (
+    t2i_modulate,
+    CaptionEmbedder,
+    AttentionKVCompress,
+    MultiHeadCrossAttention,
+    T2IFinalLayer,
+    SizeEmbedder,
+)
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
+
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
+    grid_h, grid_w = torch.meshgrid(
+        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
+        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
+        indexing='ij'
+    )
+    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
+class PixArtMSBlock(nn.Module):
+    """
+    A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
+                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.attn = AttentionKVCompress(
+            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
+            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.cross_attn = MultiHeadCrossAttention(
+            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        # to be compatible with lower version pytorch
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(
+            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
+
+    def forward(self, x, y, t, mask=None, HW=None, **kwargs):
+        B, N, C = x.shape
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
+        x = x + self.cross_attn(x, y, mask)
+        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+
+        return x
+
+
+### Core PixArt Model ###
+class PixArtMS(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+            self,
+            input_size=32,
+            patch_size=2,
+            in_channels=4,
+            hidden_size=1152,
+            depth=28,
+            num_heads=16,
+            mlp_ratio=4.0,
+            class_dropout_prob=0.1,
+            learn_sigma=True,
+            pred_sigma=True,
+            drop_path: float = 0.,
+            caption_channels=4096,
+            pe_interpolation=None,
+            pe_precision=None,
+            config=None,
+            model_max_length=120,
+            micro_condition=True,
+            qk_norm=False,
+            kv_compress_config=None,
+            dtype=None,
+            device=None,
+            operations=None,
+            **kwargs,
+    ):
+        nn.Module.__init__(self)
+        self.dtype = dtype
+        self.pred_sigma = pred_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.pe_interpolation = pe_interpolation
+        self.pe_precision = pe_precision
+        self.hidden_size = hidden_size
+        self.depth = depth
+
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.t_block = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.x_embedder = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_channels,
+            embed_dim=hidden_size,
+            bias=True,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations,
+        )
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
+            act_layer=approx_gelu, token_num=model_max_length,
+            dtype=dtype, device=device, operations=operations,
+        )
+
+        self.micro_conditioning = micro_condition
+        if self.micro_conditioning:
+            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+
+        # For fixed sin-cos embedding:
+        # num_patches = (input_size // patch_size) * (input_size // patch_size)
+        # self.base_size = input_size // self.patch_size
+        # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+
+        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
+        if kv_compress_config is None:
+            kv_compress_config = {
+                'sampling': None,
+                'scale_factor': 1,
+                'kv_compress_layer': [],
+            }
+        self.blocks = nn.ModuleList([
+            PixArtMSBlock(
+                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
+                sampling=kv_compress_config['sampling'],
+                sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
+                qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            )
+            for i in range(depth)
+        ])
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
+
+    def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):
+        """
+        Original forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) conditioning
+        ar: (N, 1): aspect ratio
+        cs: (N ,2) size conditioning for height/width
+        """
+        B, C, H, W = x.shape
+        c_res = (H + W) // 2
+        pe_interpolation = self.pe_interpolation
+        if pe_interpolation is None or self.pe_precision is not None:
+            # calculate pe_interpolation on-the-fly
+            pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0)
+
+        pos_embed = get_2d_sincos_pos_embed_torch(
+            self.hidden_size,
+            h=(H // self.patch_size),
+            w=(W // self.patch_size),
+            pe_interpolation=pe_interpolation,
+            base_size=((round(c_res / 64) * 64) // self.patch_size),
+            device=x.device,
+            dtype=x.dtype,
+        ).unsqueeze(0)
+
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
+
+        if self.micro_conditioning and (c_size is not None and c_ar is not None):
+            bs = x.shape[0]
+            c_size = self.csize_embedder(c_size, bs)  # (N, D)
+            c_ar = self.ar_embedder(c_ar, bs)  # (N, D)
+            t = t + torch.cat([c_size, c_ar], dim=1)
+
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, D)
+
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = None
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+        for block in self.blocks:
+            x = block(x, y, t0, y_lens, (H, W), **kwargs)  # (N, T, D)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x, H, W)  # (N, out_channels, H, W)
+
+        return x
+
+    def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
+        B, C, H, W = x.shape
+
+        # Fallback for missing microconds
+        if self.micro_conditioning:
+            if c_size is None:
+                c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
+
+            if c_ar is None:
+                c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
+
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)
+
+        ## only return EPS
+        if self.pred_sigma:
+            return out[:, :self.in_channels]
+        return out
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        h = h // self.patch_size
+        w = w // self.patch_size
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs

comfy/ldm/qwen_image/controlnet.py

@@ -1,77 +0,0 @@
-import torch
-import math
-
-from .model import QwenImageTransformer2DModel
-
-
-class QwenImageControlNetModel(QwenImageTransformer2DModel):
-    def __init__(
-        self,
-        extra_condition_channels=0,
-        dtype=None,
-        device=None,
-        operations=None,
-        **kwargs
-    ):
-        super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
-        self.main_model_double = 60
-
-        # controlnet_blocks
-        self.controlnet_blocks = torch.nn.ModuleList([])
-        for _ in range(len(self.transformer_blocks)):
-            self.controlnet_blocks.append(operations.Linear(self.inner_dim, self.inner_dim, device=device, dtype=dtype))
-        self.controlnet_x_embedder = operations.Linear(self.in_channels + extra_condition_channels, self.inner_dim, device=device, dtype=dtype)
-
-    def forward(
-        self,
-        x,
-        timesteps,
-        context,
-        attention_mask=None,
-        guidance: torch.Tensor = None,
-        ref_latents=None,
-        hint=None,
-        transformer_options={},
-        **kwargs
-    ):
-        timestep = timesteps
-        encoder_hidden_states = context
-        encoder_hidden_states_mask = attention_mask
-
-        hidden_states, img_ids, orig_shape = self.process_img(x)
-        hint, _, _ = self.process_img(hint)
-
-        txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
-        txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
-        del ids, txt_ids, img_ids
-
-        hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint)
-        encoder_hidden_states = self.txt_norm(encoder_hidden_states)
-        encoder_hidden_states = self.txt_in(encoder_hidden_states)
-
-        if guidance is not None:
-            guidance = guidance * 1000
-
-        temb = (
-            self.time_text_embed(timestep, hidden_states)
-            if guidance is None
-            else self.time_text_embed(timestep, guidance, hidden_states)
-        )
-
-        repeat = math.ceil(self.main_model_double / len(self.controlnet_blocks))
-
-        controlnet_block_samples = ()
-        for i, block in enumerate(self.transformer_blocks):
-            encoder_hidden_states, hidden_states = block(
-                hidden_states=hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                encoder_hidden_states_mask=encoder_hidden_states_mask,
-                temb=temb,
-                image_rotary_emb=image_rotary_emb,
-            )
-
-            controlnet_block_samples = controlnet_block_samples + (self.controlnet_blocks[i](hidden_states),) * repeat
-
-        return {"input": controlnet_block_samples[:self.main_model_double]}

comfy/ldm/qwen_image/model.py

@@ -1,473 +0,0 @@
-# https://github.com/QwenLM/Qwen-Image (Apache 2.0)
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from typing import Optional, Tuple
-from einops import repeat
-
-from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
-from comfy.ldm.modules.attention import optimized_attention_masked
-from comfy.ldm.flux.layers import EmbedND
-import comfy.ldm.common_dit
-import comfy.patcher_extension
-
-class GELU(nn.Module):
-    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.proj = operations.Linear(dim_in, dim_out, bias=bias, dtype=dtype, device=device)
-        self.approximate = approximate
-
-    def forward(self, hidden_states):
-        hidden_states = self.proj(hidden_states)
-        hidden_states = F.gelu(hidden_states, approximate=self.approximate)
-        return hidden_states
-
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        mult: int = 4,
-        dropout: float = 0.0,
-        inner_dim=None,
-        bias: bool = True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-        if inner_dim is None:
-            inner_dim = int(dim * mult)
-        dim_out = dim_out if dim_out is not None else dim
-
-        self.net = nn.ModuleList([])
-        self.net.append(GELU(dim, inner_dim, approximate="tanh", bias=bias, dtype=dtype, device=device, operations=operations))
-        self.net.append(nn.Dropout(dropout))
-        self.net.append(operations.Linear(inner_dim, dim_out, bias=bias, dtype=dtype, device=device))
-
-    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
-        for module in self.net:
-            hidden_states = module(hidden_states)
-        return hidden_states
-
-
-def apply_rotary_emb(x, freqs_cis):
-    if x.shape[1] == 0:
-        return x
-
-    t_ = x.reshape(*x.shape[:-1], -1, 1, 2)
-    t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
-    return t_out.reshape(*x.shape)
-
-
-class QwenTimestepProjEmbeddings(nn.Module):
-    def __init__(self, embedding_dim, pooled_projection_dim, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000)
-        self.timestep_embedder = TimestepEmbedding(
-            in_channels=256,
-            time_embed_dim=embedding_dim,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-
-    def forward(self, timestep, hidden_states):
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype))
-        return timesteps_emb
-
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        query_dim: int,
-        dim_head: int = 64,
-        heads: int = 8,
-        dropout: float = 0.0,
-        bias: bool = False,
-        eps: float = 1e-5,
-        out_bias: bool = True,
-        out_dim: int = None,
-        out_context_dim: int = None,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
-        self.inner_kv_dim = self.inner_dim
-        self.heads = heads
-        self.dim_head = dim_head
-        self.out_dim = out_dim if out_dim is not None else query_dim
-        self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim
-        self.dropout = dropout
-
-        # Q/K normalization
-        self.norm_q = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device)
-        self.norm_k = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device)
-        self.norm_added_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
-        self.norm_added_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device)
-
-        # Image stream projections
-        self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device)
-        self.to_k = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)
-        self.to_v = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)
-
-        # Text stream projections
-        self.add_q_proj = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device)
-        self.add_k_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)
-        self.add_v_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device)
-
-        # Output projections
-        self.to_out = nn.ModuleList([
-            operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device),
-            nn.Dropout(dropout)
-        ])
-        self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,  # Image stream
-        encoder_hidden_states: torch.FloatTensor = None,  # Text stream
-        encoder_hidden_states_mask: torch.FloatTensor = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        image_rotary_emb: Optional[torch.Tensor] = None,
-        transformer_options={},
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        seq_txt = encoder_hidden_states.shape[1]
-
-        img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1))
-        img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1))
-        img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1))
-
-        txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-        txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-        txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1))
-
-        img_query = self.norm_q(img_query)
-        img_key = self.norm_k(img_key)
-        txt_query = self.norm_added_q(txt_query)
-        txt_key = self.norm_added_k(txt_key)
-
-        joint_query = torch.cat([txt_query, img_query], dim=1)
-        joint_key = torch.cat([txt_key, img_key], dim=1)
-        joint_value = torch.cat([txt_value, img_value], dim=1)
-
-        joint_query = apply_rotary_emb(joint_query, image_rotary_emb)
-        joint_key = apply_rotary_emb(joint_key, image_rotary_emb)
-
-        joint_query = joint_query.flatten(start_dim=2)
-        joint_key = joint_key.flatten(start_dim=2)
-        joint_value = joint_value.flatten(start_dim=2)
-
-        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options)
-
-        txt_attn_output = joint_hidden_states[:, :seq_txt, :]
-        img_attn_output = joint_hidden_states[:, seq_txt:, :]
-
-        img_attn_output = self.to_out[0](img_attn_output)
-        img_attn_output = self.to_out[1](img_attn_output)
-        txt_attn_output = self.to_add_out(txt_attn_output)
-
-        return img_attn_output, txt_attn_output
-
-
-class QwenImageTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        eps: float = 1e-6,
-        dtype=None,
-        device=None,
-        operations=None
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_attention_heads = num_attention_heads
-        self.attention_head_dim = attention_head_dim
-
-        self.img_mod = nn.Sequential(
-            nn.SiLU(),
-            operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device),
-        )
-        self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.img_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations)
-
-        self.txt_mod = nn.Sequential(
-            nn.SiLU(),
-            operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device),
-        )
-        self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
-        self.txt_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations)
-
-        self.attn = Attention(
-            query_dim=dim,
-            dim_head=attention_head_dim,
-            heads=num_attention_heads,
-            out_dim=dim,
-            bias=True,
-            eps=eps,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-        )
-
-    def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        shift, scale, gate = torch.chunk(mod_params, 3, dim=-1)
-        return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_hidden_states_mask: torch.Tensor,
-        temb: torch.Tensor,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        transformer_options={},
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        img_mod_params = self.img_mod(temb)
-        txt_mod_params = self.txt_mod(temb)
-        img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)
-        txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)
-
-        img_normed = self.img_norm1(hidden_states)
-        img_modulated, img_gate1 = self._modulate(img_normed, img_mod1)
-        txt_normed = self.txt_norm1(encoder_hidden_states)
-        txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1)
-
-        img_attn_output, txt_attn_output = self.attn(
-            hidden_states=img_modulated,
-            encoder_hidden_states=txt_modulated,
-            encoder_hidden_states_mask=encoder_hidden_states_mask,
-            image_rotary_emb=image_rotary_emb,
-            transformer_options=transformer_options,
-        )
-
-        hidden_states = hidden_states + img_gate1 * img_attn_output
-        encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output
-
-        img_normed2 = self.img_norm2(hidden_states)
-        img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2)
-        hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2))
-
-        txt_normed2 = self.txt_norm2(encoder_hidden_states)
-        txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2)
-        encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2))
-
-        return encoder_hidden_states, hidden_states
-
-
-class LastLayer(nn.Module):
-    def __init__(
-        self,
-        embedding_dim: int,
-        conditioning_embedding_dim: int,
-        elementwise_affine=False,
-        eps=1e-6,
-        bias=True,
-        dtype=None, device=None, operations=None
-    ):
-        super().__init__()
-        self.silu = nn.SiLU()
-        self.linear = operations.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias, dtype=dtype, device=device)
-        self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine=False, bias=bias, dtype=dtype, device=device)
-
-    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
-        emb = self.linear(self.silu(conditioning_embedding))
-        scale, shift = torch.chunk(emb, 2, dim=1)
-        x = torch.addcmul(shift[:, None, :], self.norm(x), (1 + scale)[:, None, :])
-        return x
-
-
-class QwenImageTransformer2DModel(nn.Module):
-    def __init__(
-        self,
-        patch_size: int = 2,
-        in_channels: int = 64,
-        out_channels: Optional[int] = 16,
-        num_layers: int = 60,
-        attention_head_dim: int = 128,
-        num_attention_heads: int = 24,
-        joint_attention_dim: int = 3584,
-        pooled_projection_dim: int = 768,
-        guidance_embeds: bool = False,
-        axes_dims_rope: Tuple[int, int, int] = (16, 56, 56),
-        image_model=None,
-        final_layer=True,
-        dtype=None,
-        device=None,
-        operations=None,
-    ):
-        super().__init__()
-        self.dtype = dtype
-        self.patch_size = patch_size
-        self.in_channels = in_channels
-        self.out_channels = out_channels or in_channels
-        self.inner_dim = num_attention_heads * attention_head_dim
-
-        self.pe_embedder = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope))
-
-        self.time_text_embed = QwenTimestepProjEmbeddings(
-            embedding_dim=self.inner_dim,
-            pooled_projection_dim=pooled_projection_dim,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-
-        self.txt_norm = operations.RMSNorm(joint_attention_dim, eps=1e-6, dtype=dtype, device=device)
-        self.img_in = operations.Linear(in_channels, self.inner_dim, dtype=dtype, device=device)
-        self.txt_in = operations.Linear(joint_attention_dim, self.inner_dim, dtype=dtype, device=device)
-
-        self.transformer_blocks = nn.ModuleList([
-            QwenImageTransformerBlock(
-                dim=self.inner_dim,
-                num_attention_heads=num_attention_heads,
-                attention_head_dim=attention_head_dim,
-                dtype=dtype,
-                device=device,
-                operations=operations
-            )
-            for _ in range(num_layers)
-        ])
-
-        if final_layer:
-            self.norm_out = LastLayer(self.inner_dim, self.inner_dim, dtype=dtype, device=device, operations=operations)
-            self.proj_out = operations.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True, dtype=dtype, device=device)
-
-    def process_img(self, x, index=0, h_offset=0, w_offset=0):
-        bs, c, t, h, w = x.shape
-        patch_size = self.patch_size
-        hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size))
-        orig_shape = hidden_states.shape
-        hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2)
-        hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5)
-        hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4)
-        h_len = ((h + (patch_size // 2)) // patch_size)
-        w_len = ((w + (patch_size // 2)) // patch_size)
-
-        h_offset = ((h_offset + (patch_size // 2)) // patch_size)
-        w_offset = ((w_offset + (patch_size // 2)) // patch_size)
-
-        img_ids = torch.zeros((h_len, w_len, 3), device=x.device)
-        img_ids[:, :, 0] = img_ids[:, :, 1] + index
-        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - (h_len // 2)
-        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - (w_len // 2)
-        return hidden_states, repeat(img_ids, "h w c -> b (h w) c", b=bs), orig_shape
-
-    def forward(self, x, timestep, context, attention_mask=None, guidance=None, ref_latents=None, transformer_options={}, **kwargs):
-        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
-            self._forward,
-            self,
-            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, attention_mask, guidance, ref_latents, transformer_options, **kwargs)
-
-    def _forward(
-        self,
-        x,
-        timesteps,
-        context,
-        attention_mask=None,
-        guidance: torch.Tensor = None,
-        ref_latents=None,
-        transformer_options={},
-        control=None,
-        **kwargs
-    ):
-        timestep = timesteps
-        encoder_hidden_states = context
-        encoder_hidden_states_mask = attention_mask
-
-        hidden_states, img_ids, orig_shape = self.process_img(x)
-        num_embeds = hidden_states.shape[1]
-
-        if ref_latents is not None:
-            h = 0
-            w = 0
-            index = 0
-            index_ref_method = kwargs.get("ref_latents_method", "index") == "index"
-            for ref in ref_latents:
-                if index_ref_method:
-                    index += 1
-                    h_offset = 0
-                    w_offset = 0
-                else:
-                    index = 1
-                    h_offset = 0
-                    w_offset = 0
-                    if ref.shape[-2] + h > ref.shape[-1] + w:
-                        w_offset = w
-                    else:
-                        h_offset = h
-                    h = max(h, ref.shape[-2] + h_offset)
-                    w = max(w, ref.shape[-1] + w_offset)
-
-                kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
-                hidden_states = torch.cat([hidden_states, kontext], dim=1)
-                img_ids = torch.cat([img_ids, kontext_ids], dim=1)
-
-        txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2))
-        txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype)
-        del ids, txt_ids, img_ids
-
-        hidden_states = self.img_in(hidden_states)
-        encoder_hidden_states = self.txt_norm(encoder_hidden_states)
-        encoder_hidden_states = self.txt_in(encoder_hidden_states)
-
-        if guidance is not None:
-            guidance = guidance * 1000
-
-        temb = (
-            self.time_text_embed(timestep, hidden_states)
-            if guidance is None
-            else self.time_text_embed(timestep, guidance, hidden_states)
-        )
-
-        patches_replace = transformer_options.get("patches_replace", {})
-        patches = transformer_options.get("patches", {})
-        blocks_replace = patches_replace.get("dit", {})
-
-        for i, block in enumerate(self.transformer_blocks):
-            if ("double_block", i) in blocks_replace:
-                def block_wrap(args):
-                    out = {}
-                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"])
-                    return out
-                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
-                hidden_states = out["img"]
-                encoder_hidden_states = out["txt"]
-            else:
-                encoder_hidden_states, hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    encoder_hidden_states_mask=encoder_hidden_states_mask,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                    transformer_options=transformer_options,
-                )
-
-            if "double_block" in patches:
-                for p in patches["double_block"]:
-                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options})
-                    hidden_states = out["img"]
-                    encoder_hidden_states = out["txt"]
-
-            if control is not None: # Controlnet
-                control_i = control.get("input")
-                if i < len(control_i):
-                    add = control_i[i]
-                    if add is not None:
-                        hidden_states[:, :add.shape[1]] += add
-
-        hidden_states = self.norm_out(hidden_states, temb)
-        hidden_states = self.proj_out(hidden_states)
-
-        hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2)
-        hidden_states = hidden_states.permute(0, 3, 1, 4, 2, 5)
-        return hidden_states.reshape(orig_shape)[:, :, :, :x.shape[-2], :x.shape[-1]]

comfy/ldm/wan/model_animate.py

@@ -1,548 +0,0 @@
-from torch import nn
-import torch
-from typing import Tuple, Optional
-from einops import rearrange
-import torch.nn.functional as F
-import math
-from .model import WanModel, sinusoidal_embedding_1d
-from comfy.ldm.modules.attention import optimized_attention
-import comfy.model_management
-
-class CausalConv1d(nn.Module):
-
-    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", operations=None, **kwargs):
-        super().__init__()
-
-        self.pad_mode = pad_mode
-        padding = (kernel_size - 1, 0)  # T
-        self.time_causal_padding = padding
-
-        self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
-
-    def forward(self, x):
-        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
-        return self.conv(x)
-
-
-class FaceEncoder(nn.Module):
-    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None):
-        factory_kwargs = {"dtype": dtype, "device": device}
-        super().__init__()
-
-        self.num_heads = num_heads
-        self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs)
-        self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
-        self.act = nn.SiLU()
-        self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
-        self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
-
-        self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs)
-        self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
-
-        self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
-
-        self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
-
-        self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs))
-
-    def forward(self, x):
-
-        x = rearrange(x, "b t c -> b c t")
-        b, c, t = x.shape
-
-        x = self.conv1_local(x)
-        x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
-
-        x = self.norm1(x)
-        x = self.act(x)
-        x = rearrange(x, "b t c -> b c t")
-        x = self.conv2(x)
-        x = rearrange(x, "b c t -> b t c")
-        x = self.norm2(x)
-        x = self.act(x)
-        x = rearrange(x, "b t c -> b c t")
-        x = self.conv3(x)
-        x = rearrange(x, "b c t -> b t c")
-        x = self.norm3(x)
-        x = self.act(x)
-        x = self.out_proj(x)
-        x = rearrange(x, "(b n) t c -> b t n c", b=b)
-        padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1)
-        x = torch.cat([x, padding], dim=-2)
-        x_local = x.clone()
-
-        return x_local
-
-
-def get_norm_layer(norm_layer, operations=None):
-    """
-    Get the normalization layer.
-
-    Args:
-        norm_layer (str): The type of normalization layer.
-
-    Returns:
-        norm_layer (nn.Module): The normalization layer.
-    """
-    if norm_layer == "layer":
-        return operations.LayerNorm
-    elif norm_layer == "rms":
-        return operations.RMSNorm
-    else:
-        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
-
-
-class FaceAdapter(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        heads_num: int,
-        qk_norm: bool = True,
-        qk_norm_type: str = "rms",
-        num_adapter_layers: int = 1,
-        dtype=None, device=None, operations=None
-    ):
-
-        factory_kwargs = {"dtype": dtype, "device": device}
-        super().__init__()
-        self.hidden_size = hidden_dim
-        self.heads_num = heads_num
-        self.fuser_blocks = nn.ModuleList(
-            [
-                FaceBlock(
-                    self.hidden_size,
-                    self.heads_num,
-                    qk_norm=qk_norm,
-                    qk_norm_type=qk_norm_type,
-                    operations=operations,
-                    **factory_kwargs,
-                )
-                for _ in range(num_adapter_layers)
-            ]
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        motion_embed: torch.Tensor,
-        idx: int,
-        freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
-        freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
-    ) -> torch.Tensor:
-
-        return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)
-
-
-
-class FaceBlock(nn.Module):
-    def __init__(
-        self,
-        hidden_size: int,
-        heads_num: int,
-        qk_norm: bool = True,
-        qk_norm_type: str = "rms",
-        qk_scale: float = None,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-        operations=None
-    ):
-        factory_kwargs = {"device": device, "dtype": dtype}
-        super().__init__()
-
-        self.deterministic = False
-        self.hidden_size = hidden_size
-        self.heads_num = heads_num
-        head_dim = hidden_size // heads_num
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
-        self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
-
-        self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
-
-        qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations)
-        self.q_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
-        )
-        self.k_norm = (
-            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
-        )
-
-        self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
-
-        self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        motion_vec: torch.Tensor,
-        motion_mask: Optional[torch.Tensor] = None,
-        # use_context_parallel=False,
-    ) -> torch.Tensor:
-
-        B, T, N, C = motion_vec.shape
-        T_comp = T
-
-        x_motion = self.pre_norm_motion(motion_vec)
-        x_feat = self.pre_norm_feat(x)
-
-        kv = self.linear1_kv(x_motion)
-        q = self.linear1_q(x_feat)
-
-        k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
-        q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)
-
-        # Apply QK-Norm if needed.
-        q = self.q_norm(q).to(v)
-        k = self.k_norm(k).to(v)
-
-        k = rearrange(k, "B L N H D -> (B L) N H D")
-        v = rearrange(v, "B L N H D -> (B L) N H D")
-
-        q = rearrange(q, "B (L S) H D -> (B L) S (H D)", L=T_comp)
-
-        attn = optimized_attention(q, k, v, heads=self.heads_num)
-
-        attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp)
-
-        output = self.linear2(attn)
-
-        if motion_mask is not None:
-            output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)
-
-        return output
-
-# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162
-def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
-    _, minor, in_h, in_w = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, minor, in_h, 1, in_w, 1)
-    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
-    out = out.view(-1, minor, in_h * up_y, in_w * up_x)
-
-    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
-    out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)]
-
-    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
-    return out[:, :, ::down_y, ::down_x]
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
-
-# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81
-class FusedLeakyReLU(torch.nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None):
-        super().__init__()
-        self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale)
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
-    return F.leaky_relu(input + bias, negative_slope) * scale
-
-class Blur(torch.nn.Module):
-    def __init__(self, kernel, pad, dtype=None, device=None):
-        super().__init__()
-        kernel = torch.tensor(kernel, dtype=dtype, device=device)
-        kernel = kernel[None, :] * kernel[:, None]
-        kernel = kernel / kernel.sum()
-        self.register_buffer('kernel', kernel)
-        self.pad = pad
-
-    def forward(self, input):
-        return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad)
-
-#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590
-class ScaledLeakyReLU(torch.nn.Module):
-    def __init__(self, negative_slope=0.2):
-        super().__init__()
-        self.negative_slope = negative_slope
-
-    def forward(self, input):
-        return F.leaky_relu(input, negative_slope=self.negative_slope)
-
-# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605
-class EqualConv2d(torch.nn.Module):
-    def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype))
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-        self.stride = stride
-        self.padding = padding
-        self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None
-
-    def forward(self, input):
-        if self.bias is None:
-            bias = None
-        else:
-            bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype)
-
-        return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding)
-
-# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134
-class EqualLinear(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype))
-        self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None
-        self.activation = activation
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.bias is None:
-            bias = None
-        else:
-            bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul
-
-        if self.activation:
-            out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale)
-            return fused_leaky_relu(out, bias)
-        return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias)
-
-# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654
-class ConvLayer(torch.nn.Sequential):
-    def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2)))
-            stride, padding = 2, 0
-        else:
-            stride, padding = 1, kernel_size // 2
-
-        layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations))
-
-        if activate:
-            layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2))
-
-        super().__init__(*layers)
-
-# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704
-class ResBlock(torch.nn.Module):
-    def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations)
-        self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations)
-
-    def forward(self, input):
-        out = self.conv2(self.conv1(input))
-        skip = self.skip(input)
-        return (out + skip) / math.sqrt(2)
-
-
-class EncoderApp(torch.nn.Module):
-    def __init__(self, w_dim=512, dtype=None, device=None, operations=None):
-        super().__init__()
-        kwargs = {"device": device, "dtype": dtype, "operations": operations}
-
-        self.convs = torch.nn.ModuleList([
-            ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs),
-            ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs),
-            ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs),
-            ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs),
-            EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs)
-        ])
-
-    def forward(self, x):
-        h = x
-        for conv in self.convs:
-            h = conv(h)
-        return h.squeeze(-1).squeeze(-1)
-
-class Encoder(torch.nn.Module):
-    def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations)
-        self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)])
-
-    def encode_motion(self, x):
-        return self.fc(self.net_app(x))
-
-class Direction(torch.nn.Module):
-    def __init__(self, motion_dim, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype))
-        self.motion_dim = motion_dim
-
-    def forward(self, input):
-        stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype)
-        Q, _ = torch.linalg.qr(stabilized_weight.float())
-        if input is None:
-            return Q
-        return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1)
-
-class Synthesis(torch.nn.Module):
-    def __init__(self, motion_dim, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations)
-
-class Generator(torch.nn.Module):
-    def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations)
-        self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations)
-
-    def get_motion(self, img):
-        motion_feat = self.enc.encode_motion(img)
-        return self.dec.direction(motion_feat)
-
-class AnimateWanModel(WanModel):
-    r"""
-    Wan diffusion backbone supporting both text-to-video and image-to-video.
-    """
-
-    def __init__(self,
-                 model_type='animate',
-                 patch_size=(1, 2, 2),
-                 text_len=512,
-                 in_dim=16,
-                 dim=2048,
-                 ffn_dim=8192,
-                 freq_dim=256,
-                 text_dim=4096,
-                 out_dim=16,
-                 num_heads=16,
-                 num_layers=32,
-                 window_size=(-1, -1),
-                 qk_norm=True,
-                 cross_attn_norm=True,
-                 eps=1e-6,
-                 flf_pos_embed_token_number=None,
-                 motion_encoder_dim=512,
-                 image_model=None,
-                 device=None,
-                 dtype=None,
-                 operations=None,
-                 ):
-
-        super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
-
-        self.pose_patch_embedding = operations.Conv3d(
-            16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype
-        )
-
-        self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations)
-
-        self.face_adapter = FaceAdapter(
-            heads_num=self.num_heads,
-            hidden_dim=self.dim,
-            num_adapter_layers=self.num_layers // 5,
-            device=device, dtype=dtype, operations=operations
-        )
-
-        self.face_encoder = FaceEncoder(
-            in_dim=motion_encoder_dim,
-            hidden_dim=self.dim,
-            num_heads=4,
-            device=device, dtype=dtype, operations=operations
-        )
-
-    def after_patch_embedding(self, x, pose_latents, face_pixel_values):
-        if pose_latents is not None:
-            pose_latents = self.pose_patch_embedding(pose_latents)
-            x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1]
-
-        if face_pixel_values is None:
-            return x, None
-
-        b, c, T, h, w = face_pixel_values.shape
-        face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")
-        encode_bs = 8
-        face_pixel_values_tmp = []
-        for i in range(math.ceil(face_pixel_values.shape[0] / encode_bs)):
-            face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i * encode_bs: (i + 1) * encode_bs]))
-
-        motion_vec = torch.cat(face_pixel_values_tmp)
-
-        motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
-        motion_vec = self.face_encoder(motion_vec)
-
-        B, L, H, C = motion_vec.shape
-        pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
-        motion_vec = torch.cat([pad_face, motion_vec], dim=1)
-
-        if motion_vec.shape[1] < x.shape[2]:
-            B, L, H, C = motion_vec.shape
-            pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec)
-            motion_vec = torch.cat([motion_vec, pad], dim=1)
-        else:
-            motion_vec = motion_vec[:, :x.shape[2]]
-        return x, motion_vec
-
-    def forward_orig(
-        self,
-        x,
-        t,
-        context,
-        clip_fea=None,
-        pose_latents=None,
-        face_pixel_values=None,
-        freqs=None,
-        transformer_options={},
-        **kwargs,
-    ):
-        # embeddings
-        x = self.patch_embedding(x.float()).to(x.dtype)
-        x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
-        grid_sizes = x.shape[2:]
-        x = x.flatten(2).transpose(1, 2)
-
-        # time embeddings
-        e = self.time_embedding(
-            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
-        e = e.reshape(t.shape[0], -1, e.shape[-1])
-        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
-
-        full_ref = None
-        if self.ref_conv is not None:
-            full_ref = kwargs.get("reference_latent", None)
-            if full_ref is not None:
-                full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
-                x = torch.concat((full_ref, x), dim=1)
-
-        # context
-        context = self.text_embedding(context)
-
-        context_img_len = None
-        if clip_fea is not None:
-            if self.img_emb is not None:
-                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
-                context = torch.concat([context_clip, context], dim=1)
-            context_img_len = clip_fea.shape[-2]
-
-        patches_replace = transformer_options.get("patches_replace", {})
-        blocks_replace = patches_replace.get("dit", {})
-        for i, block in enumerate(self.blocks):
-            if ("double_block", i) in blocks_replace:
-                def block_wrap(args):
-                    out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
-                    return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
-                x = out["img"]
-            else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
-
-            if i % 5 == 0 and motion_vec is not None:
-                x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec)
-
-        # head
-        x = self.head(x, e)
-
-        if full_ref is not None:
-            x = x[:, full_ref.shape[1]:]
-
-        # unpatchify
-        x = self.unpatchify(x, grid_sizes)
-        return x